The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Enhanced YOLOv5: An Efficient Road Object Detection Method
Open accessAbstract
Accurate identification of road objects is crucial for achieving intelligent traffic systems. However, developing efficient and accurate road object detection methods in complex traffic scenarios has always been a challenging task. The objective of this study was to improve the target detection algorithm for road object detection by enhancing the algorithm's capability to fuse features of different scales and levels, thereby improving the accurate identification of objects in complex road scenes. We propose an improved method called the Enhanced YOLOv5 algorithm for road object detection. By introducing the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5 algorithm, we address the challenges of multi-scale and multi-level feature fusion and enhance the detection capability for objects of different sizes. Additionally, we integrate the Convolutional Block Attention Module (CBAM) into the existing YOLOv5 model to enhance its feature representation capability. Furthermore, we employ a new non-maximum suppression technique called Distance Intersection Over Union (DIOU) to effectively address issues such as misjudgment and duplicate detection when significant overlap occurs between bounding boxes. We use mean Average Precision (mAP) and Precision (P) as evaluation metrics. Finally, experimental results on the BDD100K dataset demonstrate that the improved YOLOv5 algorithm achieves a 1.6% increase in object detection mAP, while the P value increases by 5.3%, effectively improving the accuracy and robustness of road object recognition.
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“It often employs techniques like Feature Pyramid Networks (FPN) and Spatial Attention Modules (SAM) [41] to improve feature representation.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [41]
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
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
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
CBAM: Convolutional Block Attention Module
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072
Path Aggregation Network for Instance Segmentation
Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00913 · Cited by 10,011
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
EfficientDet: Scalable and Efficient Object Detection
Tan, M., Pang, R., & Le, Q. V. (2019) · arXiv · Cited by 9,841
Flagged first, then by citations
Research works printed but not linked: 8
- Sifre, L. (2014). Rigid-Motion Scattering for Image Classifification. [Ph.D. Thesis, École Polytechnique].
- Liu, S., Huang, D., and Wang, Y. (2019). Learning spatial fusion for single-shot object detection. arXiv.
- Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv.
- Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv.
- Bochkovskiy, A., Wang, C., and Liao, H. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv.
- Terven, J., and Cordova-Esparza, D. (2023). A Comprehensive Review of YOLO: From YOLOv1 and Beyond. arXiv.
- Zagoruyko, S., and Komodakis, N. (2016). Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv.
- Küppers, F., Haselhoff, A., Kronenberger, J., and Schneider, J. (2022). Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety, Springer.
Cited by
Sorted by citations
Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383
Liu, S., Chen, R., Ye, M., Luo, J., Yang, D., & Dai, M. (2024) · Sensors · doi:10.3390/s24144666 · Cited by 42
QL-YOLOv8s: Precisely Optimized Lightweight YOLOv8 Pavement Disease Detection Model
Jinbo, G., Wang, S., Xiaohui, C., Wang, C., & Zhang, W. (2024) · IEEE Access · doi:10.1109/access.2024.3452129 · Cited by 23
Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments
Yu, Q., Han, Y., Gao, X., Lin, W., & Han, Y. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12101754 · Cited by 23
Detecting and classifying breast masses via YOLO-based deep learning
Karaca, B. K., Telatar, Z., Güney, S., & Dengiz, B. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11153-1 · Cited by 22
Metadata
- Authors
- Chen, H., Chen, Z., & Yu, H.
- Affiliations
- Tianjin Chengjian University (CN)
- Corresponding author
- Chen, Zhan
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-10-10
- Volume
- 23
- Issue
- 20
- Pages
- 8355
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10611198
- OpenAlex
- W4387494972
- Cited by
- 49
- References
- 34
- Citations by year
- 2026: 5 · 2025: 23 · 2024: 21
- FWCI
- 4.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- traffic object detection, YOLOv5, BiFPN, CBAM, multi-scale feature fusion, intelligent transportation systems, bounding box overlap
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