The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
YOLOv3 and YOLOv4: Multiple Object Detection for Surveillance Applications
Abstract
Object detection algorithm such as You Only Look Once (YOLOv3 and YOLOv4) is implemented for traffic and surveillance applications. A neural network consists of input with minimum one hidden and output layer. Multiple object dataset (KITTI image and video), which consists of classes of images such as Car, truck, person, and two-wheeler captured during RGB and grayscale images. The dataset is composed (image and video) of varying illumination. YOLO model variants such as YOLOv3 is implemented for image and YOLOv4 for video dataset. Obtained results show that the algorithm effectively detects the objects approximately with an accuracy of 98% for image dataset and 99% for video dataset.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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 the citing paper
“Since its inception, the YOLO architecture has undergone continuous evolution, with each version from YOLOv1 through to the latest YOLOv9 introducing enhancements in accuracy, speed, and efficiency [37].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [37]
In the citing paper
“Additionally, each YOLO iteration features optimized backbones that contribute to faster processing times and better detection capabilities.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [37]
In the citing paper
“Significant advancements include the introduction of anchor boxes, which improve the model's ability to detect objects of varying shapes and sizes, as well as new loss functions aimed at optimizing performance.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [37]
Citations
Cites
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
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141
YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307
A Guide to Convolutional Neural Networks for Computer Vision
Khan, S. H., Rahmani, H., Shah, S. A. A., & Bennamoun, M. (2018) · Synthesis lectures on computer vision · doi:10.2200/s00822ed1v01y201712cov015 · Cited by 727
Feature Extraction and Image Recognition with Convolutional Neural Networks
Liu, Y. H. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1087/6/062032 · Cited by 181
Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications
Jain, H., Vikram, A., Mohana, Kashyap, A., & Jain, A. (2020) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc48915.2020.9155832 · Cited by 124
Self-citationOptimized YOLOv3 Algorithm and Its Application in Traffic Flow Detections
Huang, Y., Zheng, J., Sun, S., Yang, C., & Liu, J. (2020) · Applied Sciences · doi:10.3390/app10093079 · Cited by 115
Traffic Signal Violation Detection using Artificial Intelligence and Deep Learning
Franklin, R. J., & Mohana (2020) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces48766.2020.9137873 · Cited by 100
Self-citationClassification of Objects in Video Records using Neural Network Framework
Biswas, A., Jana, A. P., Mohana, & Tejas, S. S. (2018) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit.2018.8748560 · Cited by 44
Self-citationNehashree, M. R., Pallavi Raj, S., & Mohana (2019) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iss1.2019.8907990 · Cited by 42
Self-citationJain, N., Yerragolla, S., Guha, T., & Mohana (2019) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac47947.2019.9032502 · Cited by 31
Self-citationFace Recognition Using Machine Learning
Singh, A. K., Kumar, S., Kumar, B., & Chauhan, A. S. (2023) · International Conference on Sustainable Emerging Innovations in Engineering and Technology (ICSEIET) · doi:10.1109/icseiet58677.2023.10303390 · Cited by 31
Real-time implementation of object detection and tracking on DSP for video surveillance applications
Mankani, S. K., Kumar, N. S., Dongrekar, P. R., Sajjanar, S., Mohana, & Aradhya, H. V. R. (2016) · IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology · doi:10.1109/rteict.2016.7808180 · Cited by 26
Self-citationPerformance Analysis of Object Detection Algorithm for Intelligent Traffic Surveillance System
Chethan Kumar, B., Punitha, R., & Mohana (2020) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca48905.2020.9182793 · Cited by 25
Self-citationInterfacing of flash memory and DDR3 RAM memory with Kintex 7 FPGA board
Akash, D., Kishore, M., Mohana, & Basha, K. H. (2017) · IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology · doi:10.1109/rteict.2017.8256950 · Cited by 20
Self-citationA vehicle detection using selective multi-stage features in convolutional neural networks
Lee, W. J., Pae, D.-S., Kim, D.-W., & Lim, M. T. (2017) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas.2017.8204413 · Cited by 7
Cited by
Sorted by citations
Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383
“Table 3 provides a comparison of the strengths and weaknesses of various YOLO variants, showcasing the tradeoffs between speed, accuracy, and complexity.”
T-YOLO: Tiny Vehicle Detection Based on YOLO and Multi-Scale Convolutional Neural Networks
Padilla Carrasco, D., Rashwan, H. A., García, M. Á., & Puig, D. (2023) · IEEE Access · doi:10.1109/access.2021.3137638 · Cited by 145
“The first section, the backbone (i.e., called cspdarknet [22]), is composed, in the case of Yolo-v5, of the most common operations in CNNs (e.g., convolutions, concatenations, max-pooling) and a simple forwarding mechanism that is constructed to extract multiple features for the next section.”
BackgroundPradhyumna, P., Shreya, G., & Mohana (2021) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc51422.2021.9532631 · Cited by 93
Self-citationQuantum Convolutional Neural Networks (QCNN) Using Deep Learning for Computer Vision Applications
Rajesh, V., Naik, U. P., & Mohana (2021) · International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) · doi:10.1109/rteict52294.2021.9574030 · Cited by 77
“Though QCNN uses architecture analogous to CNN, it uses qubits to develop a neural network between the stacked layers of the CNN architecture [34] [35] [36].”
MethodSelf-citationSwin transformer based vehicle detection in undisciplined traffic environment
Deshmukh, P., Satyanarayana, G. S. R., Majhi, S., Sahoo, U. K., & Das, S. K. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118992 · Cited by 63
Metadata
- Authors
- Kumar B., C., Punitha, R., & Mohana
- Affiliations
- R.V. College of Engineering (IN)
- ORCID
- Mohana
- Venue
- 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT)
- Publisher
- IEEE
- Published
- 2020-08
- Pages
- 1316-1321
- OpenAlex
- W3092269081
- Cited by
- 101
- References
- 16
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 21 · 2023: 21 · 2022: 26 · 2021: 23
- FWCI
- 5.40
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Smart Systems and Inventive Technology (ICSSIT) (publishedVersion)
- 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 › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- YOLOv3, YOLOv4, object detection, multiple object detection, video surveillance, traffic monitoring, KITTI dataset, car detection, human detection, grayscale images, illumination variation
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
