Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos with deep learning
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In low-brightness environment, DaCoLT detects knives with accuracy 87.74%.
“In low-brightness environment, DaCoLT detects knifes with an accuracy 87.74%.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [128]
DaCoLT is a pre-processing technique proposed in 2019 that utilizes brightness guidance to overcome negative effects of varying illumination.
“This study proposed a new pre-processing technique called darkening and contrast at learning and test (DaCoLT) in 2019 that utilizes brightness guidance to overcome the negative effects of varying illumination.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [128]
After applying DaCoLT, a CNN-based detection model trained and evaluated on low-quality videos demonstrated high potential as an automatic alarm system by satisfactory results.
“After it applied DaCoLT, a CNN-based detection model was trained and evaluated on low-quality videos, and it demonstrated high potential as an automatic alarm system by its satisfactory results.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [128]
DaCoLT's frame processing time might experience delays due to sudden knife movements.
“Additionally, the frame processing time of DaCoLT might experience delays due to sudden knife movements.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [128]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works
(2016) · Cited by 31,590 · see citing works
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works
Proceedings European Conference on Computer Vision 2002
Heyden, A., Sparr, G., Nielsen, M., & Johansen, P. (2002) · Lund University Publications (Lund University) · Cited by 5,153 · see citing works
R-FCN: Object Detection via Region-based Fully Convolutional NetworksPreprint
Dai, J., Li, Y., He, K., & Sun, J. Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06409 · Cited by 5,135 · see citing works
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239 · see citing works
Reza, A. M. (2004) · The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology · doi:10.1023/b:vlsi.0000028532.53893.82 · Cited by 1,818 · see citing works
Facial expression recognition via learning deep sparse autoencoders
Zeng, N., Zhang, H., Song, B., Liu, W., Li, Y., & Dobaie, A. M. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.08.043 · Cited by 488 · see citing works
Automatic handgun detection alarm in videos using deep learning
Olmos, R., Tabik, S., & Herrera, F. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.05.012 · Cited by 272 · see citing works
Self-citationAutomated Detection of Firearms and Knives in a CCTV Image
Grega, M., Matiolański, A., Guzik, P., & Leszczuk, M. (2015) · Sensors · doi:10.3390/s16010047 · Cited by 185 · see citing works
Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip
Zeng, N., Wang, Z., Zhang, H., Liu, W., & Alsaadi, F. E. (2016) · Cognitive Computation · doi:10.1007/s12559-016-9404-x · Cited by 159 · see citing works
A Computer Vision based Framework for Visual Gun Detection Using Harris Interest Point Detector
Tiwari, R. K., & Verma, G. K. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.06.083 · Cited by 134 · see citing works
Flitton, G. T., Breckon, T. P., & Megherbi, N. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.02.008 · Cited by 114 · see citing works
A snapshot of image pre-processing for convolutional neural networks: case study of MNIST
Tabik, S., Peralta, D., Herrera-Poyatos, A., & Herrera, F. (2016) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.2017.10.1.38 · Cited by 91 · see citing works
Self-citationVisual detection of knives in security applications using Active Appearance Models
Głowacz, A., Kmieć, M., & Dziech, A. (2013) · Multimedia Tools and Applications · doi:10.1007/s11042-013-1537-2 · Cited by 63 · see citing works
A Preliminary Approach to Intelligent X-ray Imaging for Baggage Inspection at Airports
Uroukov, I. S., & Speller, R. D. (2015) · Signal processing research · doi:10.14355/spr.2015.04.001 · Cited by 40 · see citing works
Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST '12)
Vonikakis, V., Chrysostomou, D., Kouskouridas, R., & Gasteratos, A. C. (2012) · Cited by 39 · see citing works
Object detection in security applications using dominant edge directions
Kmieć, M., & Głowacz, A. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.09.018 · Cited by 33 · see citing works
A robust object detector: Application to detection of visual knives
Buckchash, H., & Raman, B. (2017) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2017.8026214 · Cited by 32 · see citing works
Facial landmark localization by enhanced convolutional neural network
Deng, W., Fang, Y., Xu, Z., & Hu, J. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.07.052 · Cited by 28 · see citing works
Automatic detection of concealed pistols using passive millimeter wave imaging
Xiao, Z.-L., Lu, X., Yan, J., Wu, L., & Ren, L.-Y. (2015) · International Symposium on Telecommunications · doi:10.1109/ist.2015.7294538 · Cited by 27 · see citing works
A complementary facial representation extracting method based on deep learning
Sun, W., Zhao, H., & Jin, Z. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.04.063 · Cited by 26 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Pérez-Hernández, F., Tabik, S., Lamas, A., Olmos, R., Fujita, H., & Herrera, F. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105590 · Cited by 196 · see citing works
Self-citationWeapon Detection Using YOLO V3 for Smart Surveillance System
Narejo, S., Pandey, B., Esenarro, D., Rodriguez, C. R., & Anjum, M. R. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/9975700 · Cited by 160 · see citing works
Real-time gun detection in CCTV: An open problem
González, J. L. S., Zaccaro, C., Álvarez-García, J. A., Morillo, L. M. S., & Caparrini, F. S. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.09.013 · Cited by 130 · see citing works
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
“The probability of false positives that occur due to problems in illumination was discussed in [128].”
BackgroundA Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets
Shidik, G. F., Noersasongko, E., Nugraha, A., Andono, P. N., Jumanto, J., & Kusuma, E. J. (2019) · IEEE Access · doi:10.1109/access.2019.2955387 · Cited by 114 · see citing works
Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s
Ashraf, A. H., Imran, M. A., Qahtani, A. M., Alsufyani, A., Almutiry, O., Mahmood, A., Attique, M., & Habib, M. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.018785 · Cited by 103 · see citing works
Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities
Ingle, P. Y., & Kim, Y.-G. (2022) · Sensors · doi:10.3390/s22103862 · Cited by 96 · see citing works
Detection and Classification of Different Weapon Types Using Deep Learning
Kaya, V., Tuncer, S., & Baran, A. (2021) · Applied Sciences · doi:10.3390/app11167535 · Cited by 84 · see citing works
Deep Learning for Automatic Violence Detection: Tests on the AIRTLab Dataset
Sernani, P., Falcionelli, N., Tomassini, S., Contardo, P., & Dragoni, A. F. (2021) · IEEE Access · doi:10.1109/access.2021.3131315 · Cited by 77 · see citing works
Yadav, P., Gupta, N., & Sharma, P. K. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118698 · Cited by 77 · see citing works
Human pose estimation for mitigating false negatives in weapon detection in video-surveillance
Lamas, A., Tabik, S., Montes, A., Pérez-Hernández, F., García, J., Olmos, R., & Herrera, F. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.12.059 · Cited by 76 · see citing works
Self-citationHandgun Detection Using Combined Human Pose and Weapon Appearance
Ruiz-Santaquiteria, J., Velasco-Mata, A., Vállez, N., Bueno, G., Álvarez-García, J. A., & Déniz, Ó. (2021) · IEEE Access · doi:10.1109/access.2021.3110335 · Cited by 73 · see citing works
A dataset for automatic violence detection in videos
Bianculli, M., Falcionelli, N., Sernani, P., Tomassini, S., Contardo, P., Lombardi, M., & Dragoni, A. F. (2020) · Data in Brief · doi:10.1016/j.dib.2020.106587 · Cited by 69 · see citing works
Fighting against terrorism: A real-time CCTV autonomous weapons detection based on improved YOLO v4
Wang, G., Ding, H., Duan, M., Pu, Y., Yang, Z., & Li, H. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103790 · Cited by 62 · see citing works
Systematic review on weapon detection in surveillance footage through deep learning
Santos, T., Oliveira, H. P., & Cunha, A. M. T. D. S. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100612 · Cited by 53 · see citing works
Lamas, A., Tabik, S., Cruz, P., Montes, R., Martínez-Sevilla, Á., Cruz, T., & Herrera, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.09.041 · Cited by 45 · see citing works
Self-citationIoT Based Weapons Detection System for Surveillance and Security Using YOLOV4
Singh, A., Anand, T., Sharma, S., & Singh, P. P. (2021) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces51350.2021.9489224 · Cited by 40 · see citing works
A neural network aided attuned scheme for gun detection in video surveillance images
Manikandan, V. P., & Rahamathunnisa, U. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2022.104406 · Cited by 37 · see citing works
Video surveillance in smart cities: current status, challenges & future directions
Sharma, H., & Kanwal, N. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19696-6 · Cited by 36 · see citing works
Robust weapon detection in dark environments using Yolov7-DarkVision
Yadav, P., Gupta, N., & Sharma, P. K. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104342 · Cited by 35 · see citing works
Automatic Handgun and Knife Detection Algorithms: A Review
Warsi, A., Abdullah, M., Husen, M. N., & Yahya, M. (2020) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom48794.2020.9001725 · Cited by 29 · see citing works
Debnath, R., & Bhowmik, M. K. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103165 · Cited by 29 · see citing works
YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s)
Oguine, K. J., Oguine, O. C., & Bisallah, H. I. (2022) · Information Technology for Education and Development (ITED) · doi:10.1109/ited56637.2022.10051233 · Cited by 29 · see citing works
Weapon detection system for surveillance and security
Khalid, S., Waqar, A., Tahir, H. U. A., Edo, O. C., & Tenebe, I. T. (2023) · International Conference on IT Innovation and Knowledge Discovery (ITIKD) · doi:10.1109/itikd56332.2023.10099733 · Cited by 29 · see citing works
Automatic Gun Detection Approach for Video Surveillance
el den Mohamed, M. K., Taha, A., & Zayed, H. H. (2019) · International Journal of Sociotechnology and Knowledge Development · doi:10.4018/ijskd.2020010103 · Cited by 26 · see citing works
Weapon Classification using Deep Convolutional Neural Network
Dwivedi, N., Singh, D. K., & Kushwaha, D. S. (2019) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/cict48419.2019.9066227 · Cited by 25 · see citing works
Concealed pistol detection from thermal images with deep neural networks
Veranyurt, O., & Sakar, C. O. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15358-1 · Cited by 23 · see citing works
Galab, M. K., Taha, A., & Zayed, H. H. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-05401-4 · Cited by 22 · see citing works
Intelligent Video Surveillance Based on YOLO: A Comparative Study
Garg, R., & Singh, S. (2021) · International Conference on Advances in Computing, Communication and Control (ICAC3) · doi:10.1109/icac353642.2021.9697321 · Cited by 21 · see citing works
Weapon Detection from Surveillance Images using Deep Learning
Goenka, A., & Sitara, K. (2022) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet54531.2022.9824281 · Cited by 20 · see citing works
Violence Detection Using Computer Vision Approaches
Talha, K. R., Bandapadya, K., & Khan, M. M. (2022) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot54504.2022.9817374 · Cited by 18 · see citing works
Low-Light Image and Video Enhancement for More Robust Computer Vision Tasks: A Review
Tatana, M. M., Tšoeu, M. S., & Maswanganyi, R. C. (2025) · Journal of Imaging · doi:10.3390/jimaging11040125 · Cited by 17 · see citing works
Kashika, P. H., & Venkatapur, R. B. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-01062-0 · Cited by 17 · see citing works
Wajid, M. S., Terashima‐Marín, H., Najafirad, P., Conant-Pablos, S. E., & Wajid, M. A. (2024) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2024.100297 · Cited by 17 · see citing works
Zheng, T., Gao, J., Wang, Z., Wei, Y., & Dou, H. (2019) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2019.06.160 · Cited by 17 · see citing works
Yadav, P., Gupta, N., & Sharma, P. K. (2025) · International Journal of Information Technology · doi:10.1007/s41870-024-02375-y · Cited by 15 · see citing works
Vijayakumar, K. P., Pradeep, K., Balasundaram, A., & Dhande, A. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023956 · Cited by 14 · see citing works
An Enhanced Weapon Detection System using Deep Learning
Murugaiyan, S., Ruthwik, M. S., Amruth, G., & Bellam, K. (2024) · International Conference on Networking and Communications (ICNWC) · doi:10.1109/icnwc60771.2024.10537568 · Cited by 14 · see citing works
Deep Neural Networks Based on Transfer Learning Approaches to Classification of Gun and Knife Images
AĞDAŞ, M. T., Türkoğlu, M., & Gülseçen, S. (2021) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis.04.01.891308 · Cited by 13 · see citing works
Deep Learning-Based Real-Time Weapon Detection System
Al-Mousa, A. A., Alzaibaq, O. Z., & Abu-Hashyeh, Y. (2023) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/140141 · Cited by 11 · see citing works
Yue, Y., & Yang, Y. (2020) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict48043.2020.9112444 · Cited by 11 · see citing works
Weapons Detection System Based on Edge Computing and Computer Vision
Burnayev, Z. R., Toibazarov, D., Atanov, S. K., Canbolat, H., Seitbattalov, Z. Y., & Kassenov, D. D. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140586 · Cited by 10 · see citing works
Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
Torregrosa-Domínguez, Á., Álvarez-García, J. A., González, J. L. S., & Morillo, L. M. S. (2024) · Applied Sciences · doi:10.3390/app14188198 · Cited by 10 · see citing works
Towards Proactive Surveillance through CCTV Cameras under Edge-Computing and Deep Learning
Jaleel, A., Khurshid, S. K., Mustafa, R., Aamir, K. M., Tahir, M., & Ziar, R. A. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/7001388 · Cited by 10 · see citing works
Improved bounding box regression loss for weapon detection systems using deep learning
Sumi, L., & Dey, S. (2024) · International Journal of Information Technology · doi:10.1007/s41870-024-01889-9 · Cited by 11 · see citing works
A Deep Learning Approach to Gun Detection in Surveillance System using YOLOv9
Sumi, L., & Dey, S. (2024) · Control Instrumentation System Conference (CISCON) · doi:10.1109/ciscon62171.2024.10696152 · Cited by 9 · see citing works
Pseudo Trained YOLO R_CNN Model for Weapon Detection with a Real-Time Kaggle Dataset
Raju, A. R., Maddileti, T., Sirisha, J., Srinivas, R., & Saikumar, K. (2022) · International Journal of Integrated Engineering · doi:10.30880/ijie.2022.14.07.011 · Cited by 9 · see citing works
Detection of weapons using Efficient Net and Yolo v3
Ramon, A. O., & Barba-Guamán, L. (2021) · IEEE Latin American Conference on Computational Intelligence (LA-CCI) · doi:10.1109/la-cci48322.2021.9769779 · Cited by 9 · see citing works
Distributed Fog computing system for weapon detection and face recognition
Martínez, H., Rodriguez-Lozano, F. J., León‐García, F., Palomares, J. M., & Olivares, J. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.104026 · Cited by 8 · see citing works
Sandhu, S. K., & Bhatia, R. (2025) · SN Computer Science · doi:10.1007/s42979-025-04173-0 · Cited by 8 · see citing works
Weapon Detection using Yolov4, CNN
Belurkar, A., Waghmare, A., Mallick, S., Waghamode, N., & Totare, R. Y. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.41702 · Cited by 8 · see citing works
Damera, V. K., Vatambeti, R., Mekala, M. S., Pani, A. K., & Manjunath, C. (2023) · International Journal of Safety and Security Engineering · doi:10.18280/ijsse.130106 · Cited by 8 · see citing works
Weapon Detection in Surveillance Videos Using Deep Neural Networks
Quyyum, M. E. E., & Abdullah, M. H. L. (2022) · doi:10.2991/978-94-6463-082-4_19 · Cited by 7 · see citing works
Weapon Detection from Surveillance Footage in Real-Time Using Deep Learning
Jadhav, V., Deshmukh, R., Gupta, P., Ghogale, S., & Bodireddy, M. (2023) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea58933.2023.10392003 · Cited by 7 · see citing works
Automatic Weapon and Knife Detection System on Security Cameras: Comparative YOLO Models
AĞDAŞ, M. T., & Gülseçen, S. (2022) · European Journal of Science and Technology · doi:10.31590/ejosat.1163675 · Cited by 7 · see citing works
DeepCens : A deep learning‐based system for real‐time image and video censorship
Yüksel, A. S., & Tan, F. G. (2023) · Expert Systems · doi:10.1111/exsy.13436 · Cited by 6 · see citing works
Weapon Detection Identification and Classification for DCNN and YOLO-V5 Techniques
Kumar, J. A., Dhivya, G., Selvaraj, P., Karthikeyan, V. G., Ramesh, S. P., Lakshmi, S. J., & Saravanakumar, R. (2024) · International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS) · doi:10.1109/icicnis64247.2024.10823365 · Cited by 6 · see citing works
Begum, S. A., & Askarunisa, A. (2020) · Wireless Personal Communications · doi:10.1007/s11277-020-07627-1 · Cited by 6 · see citing works
Corral-Sanz, P., Barreiro-Garrido, Á., Moreno, A. B., & Sánchez, Á. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2381 · Cited by 5 · see citing works
Preprocessing Method Comparisons For VGG16 Fast-RCNN Pistol Detection
Li, J., Ablan, C., Wu, R., Guan, S., & Yao, J. J. (2021) · EPiC series in computing · doi:10.29007/ml35 · Cited by 5 · see citing works
Abdallah, R., Benbernou, S., Taher, Y., Younas, M., & Haque, R. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-16035-6_6 · Cited by 5 · see citing works
The Security of IP-Based Video Surveillance Systems
Kalbo, N., Mirsky, Y., Shabtai, A., & Elovici, Y. (2020) · Sensors · doi:10.3390/s20174806 · Cited by 5 · see citing works
Automatic Detection of Knives in Complex Scenes
Morán, M. B. H., Conci, A., & Sánchez, Á. (2022) · Intelligent systems reference library · doi:10.1007/978-3-031-06307-7_4 · Cited by 5 · see citing works
Detecting Subject-Weapon Visual Relationships
Truong, T., & Yanushkevich, S. N. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308574 · Cited by 5 · see citing works
Omiotek, Z. (2025) · Electronics · doi:10.3390/electronics14173540 · Cited by 4 · see citing works
Yadav, R., Halder, R., Thakur, A., & Banda, G. (2024) · Innovations in Systems and Software Engineering · doi:10.1007/s11334-024-00580-3 · Cited by 4 · see citing works
Real-time human detection for electricity conservation using pruned-SSD and arduino
Ushasukhanya, S., & Jothilakshmi, S. (2021) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v11i2.pp1510-1520 · Cited by 4 · see citing works
Weapon Detection Using Swin Transformer
Son, D. T., Tram, N. T. K., & Anh, V. T. (2023) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc58710.2023.10318911 · Cited by 4 · see citing works
Weapon Detection Using Deep Learning
Tram, N. T. K., Son, D. T., & Thái, A. V. (2023) · International Symposium on Information and Communication Technology · doi:10.1145/3628797.3628967 · Cited by 4 · see citing works
Data Processing Unit for Energy Saving in Computer Vision: Weapon Detection Use Case
Perea-Trigo, M., López-Ortiz, E. J., González, J. L. S., Álvarez-García, J. A., & Olmos, J. J. V. (2022) · Electronics · doi:10.3390/electronics12010146 · Cited by 4 · see citing works
(2022) · International journal of intelligent engineering and systems · doi:10.22266/ijies2022.0831.11 · Cited by 4 · see citing works
Olmos, R., Tabik, S., Pérez-Hernández, F., Lamas, A., & Herrera, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.11653 · Cited by 4 · see citing works
Self-citationAloraibi, A. Q., Hasoon, A. N., & Yassen, M. S. (2025) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats66542.2025.11258182 · Cited by 3 · see citing works
A novel model for higher performance object detection with deep channel attention super resolution
Malkoçoğlu, A. B. V., & Şamlı, R. (2025) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2025.102003 · Cited by 3 · see citing works
Enhancing Real-Time Detection Transformer (RT-DETR) for Handgun Detection on Nvidia Jetson
Bustamante, L., & Gutiérrez, J. C. (2025) · CLEI electronic journal · doi:10.19153/cleiej.28.3.5 · Cited by 3 · see citing works
Shah, S. A. A., Abdel-Wahab, A., Algeelani, N. A., & Najeeb, N. (2023) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v30.i1.pp528-544 · Cited by 3 · see citing works
Gun Detection System for Surveillance Cameras Using HOG-Assisted KNN Classifier
Sumi, L., & Dey, S. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-3481-2_18 · Cited by 3 · see citing works
Analyzing the impact of police mugshots in face verification for crime investigations
Contardo, P., Lorenzo, E. D., Falcionelli, N., Dragoni, A. F., & Sernani, P. (2022) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine54828.2022.9967671 · Cited by 3 · see citing works
Exploiting Deep Learning Algorithm to Understand Buildings’ Façade Characteristics
Rampini, L., Khodabakhshian, A., & Cecconi, F. R. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82199-9_25 · Cited by 3 · see citing works
Shah, I. A., Jhanjhi, N. Z., & Ujjan, R. M. A. (2024) · International Electronic Conference on Sensors and Applications (ECSA) · doi:10.3390/ecsa-11-20526 · Cited by 2 · see citing works
An efficient hybrid CNN–transformer framework for real-time weapon detection and face recognition
Shanthi, P., & Manjula, V. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1841848 · Cited by 2 · see citing works
Deep Classification of Gun Carried by Moving Persons Using Proposed TUVD-CSA Dataset
Debnath, R., & Bhowmik, M. K. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-4287-6_8 · Cited by 2 · see citing works
An Efficient approach for Firearms Detection using Machine Learning
Rahoo, A., Alvi, F. A., Rajput, U., & Halepoto, I. A. (2023) · VFAST Transactions on Software Engineering · doi:10.21015/vtse.v11i2.1467 · Cited by 2 · see citing works
Real-Time Handgun Detection Using Transformers on Nvidia Jetson AGX Xavier
Bustamante, L. A., & Gutiérrez, J. C. (2024) · Latin American Computer Conference (CLEI) · doi:10.1109/clei64178.2024.10700426 · Cited by 2 · see citing works
A State-of-the-Art Review of Deep Learning-Based Object Detection Methods and Techniques
Gupta, C., Gill, N. S., & Gulia, P. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-4152-6_35 · Cited by 2 · see citing works
Deteksi Senjata Genggam Menggunakan Faster R-CNN Inception V2
Pradana, I. C., Mulyanto, E., & Rachmadi, R. F. (2022) · Jurnal Teknik ITS · doi:10.12962/j23373539.v11i2.86587 · Cited by 2 · see citing works
Systematic Harmful Signs Detection for Women’S Safety Using Neural Networks
Reddy, U. Y., Nikhil, M., Krishna, P. S., & Srithar, S. (2023) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci56745.2023.10128298 · Cited by 2 · see citing works
Theft Detection using Deep LearningPreprint
Shirole, S. (2023) · Research Square · doi:10.21203/rs.3.rs-3540282/v1 · Cited by 2 · see citing works
Khalfaoui, A., Badri, A., & Mourabit, I. E. (2024) · International Conference on Wireless Networks and Mobile Communications (WINCOM) · doi:10.1109/wincom62286.2024.10657199 · Cited by 1 · see citing works
An Approach for Recognizing Object Using Convolutional Neural Networks for Detection of Weapons
Kumari, N., Vallamsetla, S., Ravva, R., & Nandini, S. S. (2025) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-031-72747-4_28 · Cited by 1 · see citing works
Theft detection with computer vision technique using Yolo-V7 algorithm
Palani, D., Kumar, M. G. L., Pushpakumari, A. K., Chowdary, A. N., Reddy, I. P. K., & Chidambaram, V. (2025) · AIP conference proceedings · doi:10.1063/5.0254433 · Cited by 1 · see citing works
A systematic review of multisensor methods for open-carry and concealed knife detection
Okafor, O., Adama, D. A., & Vinkemeier, D. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11626-2 · Cited by 1 · see citing works
Aftab, R. M. (2021) · Lahore Garrison University Research Journal of Computer Science and Information Technology · doi:10.54692/lgurjcsit.2021.0504248 · Cited by 1 · see citing works
Nanda, S. K., Ghai, D., & Ingole, P. V. (2022) · Journal of Cyber Security and Mobility · doi:10.13052/jcsm2245-1439.1143 · Cited by 1 · see citing works
A Preventive Approach to Weapons Detection for Children Using Quantum Deep Learning
Shah, S. A. A. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-21199-7_10 · Cited by 1 · see citing works
Weapon Classification with Xception: An Efficient Deep Learning Approach
Yeshwanth, A., & Bhuvaneswari, R. (2024) · International Conference on Integrated Circuits and Communication Systems (ICICACS) · doi:10.1109/icicacs60521.2024.10498457 · Cited by 1 · see citing works
Zero-shot neuro-symbolic parsing of body keypoints
Aljawaheri, D., Burghouts, G. J., van Woerden, J. E., Dijk, J., & Kuijf, H. J. (2024) · Electro-Optical and Infrared Systems: Technology and Applications · doi:10.1117/12.3031487 · Cited by 1 · see citing works
Advanced Dual Module Weapon Detection for Public Safety and Surveillance System
Sirigineedi, M., Sowmiya, M., Hemavathi, U., Bharathi, V. C., Karmode, S., & Sathya, R. (2024) · International Conference on Sustainable Expert Systems (ICSES) · doi:10.1109/icses63445.2024.10763055 · Cited by 1 · see citing works
Prasudha, M. L., Sukhavasi, V., Neha, K., & Lunawat, P. S. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03640-0 · Cited by 1 · see citing works
Final Thoughts: From Big Data to Smart Data
Luengo, J., García‐Gil, D., Ramírez‐Gallego, S., García, S., & Herrera, F. (2020) · Springer eBooks · doi:10.1007/978-3-030-39105-8_10 · Cited by 1 · see citing works
Self-citation
- Authors
- Castillo, A., Tabik, S., Pérez, F., Olmos, R., & Herrera, F.
- Affiliations
- Universidad de Granada (ES)
- Corresponding author
- Castillo, Alberto
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 330
- Pages
- 151-161
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2900484583
- Cited by
- 122
- References
- 31
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 22 · 2023: 20 · 2022: 23 · 2021: 19 · 2020: 10 · 2019: 4
- FWCI
- 4.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- Spanish Ministry of Science and Technology (TIN2017-89517-P)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- video surveillance, metallic object detection, illumination robustness, surface reflectance, automated alert system, convolutional neural networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
