The One-Stage Detector Algorithm Based on Background Prediction and Group Normalization for Vehicle Detection
Source abstract
Abstract
Vehicle detection in intelligent transportation systems (ITS) is a very important and challenging task in traffic monitoring. The difficulty of this task is to accurately locate and classify relatively small vehicles in complex scenes. To solve these problems, this paper proposes a modified one-stage detector based on background prediction and group normalization to realize real-time and accurate detection of traffic vehicles. The one-stage detector firstly adds a module to adjust the width and height of anchors and predict the target background, which avoids the problem of the target vehicle missing detection or wrong detection due to the influence of the complicated environments. Then, group normalization and the loss function based on weight attenuation can improve the one-stage detector performance in the training process. The experimental results on traffic monitoring datasets indicate that the improved one-stage detector is superior to the other neural network models in terms of precision at 95.78%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Video Surveillance and Tracking Methods · Traffic Prediction and Management Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
Nanjing Institute of Intelligent High-End Equipment Industry Company Limited, Nanjing 210042, China
College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
College of Civil Aviation, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China
The Center for Transport Studies, Imperial College, London SW7 2AZ, UK
School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
References (19)
Show source-provided references
1. A Space Mapping Based 0–1 Linear Model for Onboard Conflict Resolution of Heterogeneous Unmanned Aerial Vehicles10.1109/TVT.2019.2919737 ↗
2. Anala, M.R., Makker, M., and Ashok, A. (2019, January 17). Anomaly Detection in Surveillance Videos. Proceedings of the 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW), Hyderabad, India.10.1109/HiPCW.2019.00031 ↗
3. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24–29). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
4. Buzcu, I., and Alatan, A.A. (2016, January 25–28). Fisher-selective search for object detection. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.10.1109/ICIP.2016.7533037 ↗
5. Girshick, R. (2015, January 7–13). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.10.1109/ICCV.2015.169 ↗
6. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks10.1109/TPAMI.2016.2577031 ↗
7. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
8. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
9. L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks10.1109/TNNLS.2018.2876179 ↗
10. Liu, W., Anguelov, D., and Erhan, D. (2016, January 27–30). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Las Vegas, NV, USA.10.1007/978-3-319-46448-0_2 ↗
11. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLOv3: An Incremental Improvement. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.
12. Bochkovskiy, A., and Wang, C. (2020, April 23). YOLOv4: Optimal Speed and Accuracy of Object Detection. Available online: https://arxiv.org/abs/2004.10934.
13. Fast Automatic Vehicle Annotation for Urban Traffic Surveillance10.1109/TITS.2017.2740303 ↗
14. Fast Deep Neural Networks with Knowledge Guided Training and Predicted Regions of Interests for Real-Time Video Object Detection10.1109/ACCESS.2018.2795798 ↗
15. MIO-TCD: A New Benchmark Dataset for Vehicle Classification and Localization10.1109/TIP.2018.2848705 ↗
16. SINet: A Scale-Insensitive Convolutional Neural Network for Fast Vehicle Detection10.1109/TITS.2018.2838132 ↗
17. Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K. (2017, January 21–26). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.634 ↗
18. Zhao, Y., Han, R., and Rao, Y. (2019, January 14–15). A New Feature Pyramid Network for Object Detection. Proceedings of the 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), Jishou, China.10.1109/ICVRIS.2019.00110 ↗
19. Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18–22). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00913 ↗