AOYOLO Algorithm Oriented Vehicle and Pedestrian Detection in Foggy Weather

SU Jian; MAO Shiang; ZHUANG Wei

doi:10.23919/cje.2023.00.280

Article Contents

Article Navigation > Chinese Journal of Electronics > 2024 > Uncorrected proof

Jian SU, Shiang MAO, Wei ZHUANG, “AOYOLO Algorithm Oriented Vehicle and Pedestrian Detection in Foggy Weather,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–11, xxxx doi: 10.23919/cje.2023.00.280

Citation:

Jian SU, Shiang MAO, Wei ZHUANG, “AOYOLO Algorithm Oriented Vehicle and Pedestrian Detection in Foggy Weather,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–11, xxxx doi: 10.23919/cje.2023.00.280

Citation:

PDF( 6965 KB)

AOYOLO Algorithm Oriented Vehicle and Pedestrian Detection in Foggy Weather

doi: 10.23919/cje.2023.00.280

1.
Nanjing University of Information Science and Technology, Nanjing 210044, China

More Information

Author Bio:
Jian SU has been an associate professor in the School of Computer and Software at the Nanjing University of Information Science and Technology since 2017. He received his Ph.D. with distinction in Communication and Information Systems at University of Electronic Science and Technology of China (UESTC) in 2016. He holds a B.S. in Electronic and Information Engineering from Hankou university and an M.S. in Electronic Circuit and System from Central China Normal University. His current research interests cover Internet of Things, RFID, and Wireless sensors networking. He is a member of IEEE and a member of ACM. (Email: sj890718@gmail.com)

Shiang MAO was born in Hubei, China. He received the B.S. degree from China Three Gorges University,Hubei, China, in 2021, and he is currently pursuing the M.S. degree in School of Software Engineering, Nanjing University of Information Science and Technology, China. His current research interests cover artificial intelligence and object detection. (Email: geralt306a@gmail.com)

Wei ZHUANG was born in Jiangsu, China, in 1980. He received his B.S. and Ph.D. degrees from the College of Instrumentation Science and Technology at Southeast University, Nanjing, China, in 2002 and 2009, respectively. In 2008 and 2019, he was a joint Ph.D. candidate at Michigan Technological University, USA, and a visiting scholar at the University of Washington, USA. Since 2009, he has been working as an Associate Professor of the School of Computer Science at Nanjing University of Information Science and Technology (NUIST), China. He received the Best Paper Award at the IEEE International Conference on Wireless Communications and Signal Processing in 2009. His current research interests include modeling and analysis for integrated energy systems, deep learning for wearable sensor networks. (Email: zw@nuist.edu.cn)
Corresponding author: Email: zw@nuist.edu.cn
Received Date: 2023-08-12
Accepted Date: 2023-12-04

Available Online: 2024-02-28

Abstract

Abstract

In the context of complex foggy environments, the acquired images often suffer from low visibility, high noise, and loss of detailed information. The direct application of general object detection methods fails to achieve satisfactory results. To address these issues, this paper proposes a foggy object detection method based on YOLOv8n, named AOYOLO. The AOD-Net, a lightweight defogging network, is employed for data augmentation. Additionally, the ResCNet module is introduced in the backbone to better extract features from low-illumination images. The GACSP module is proposed in the neck to capture multi-scale features and effectively utilize them, thereby generating discriminative features with different scales. The detection head is improved using WiseIoU, which enhances the accuracy of object localization. Experimental evaluations are conducted on the publicly available datasets RTTS and synthetic foggy KITTI dataset. The results demonstrate that the proposed AOYOLO algorithm outperforms the original YOLOv8n algorithm with an average mean average precision (mAP) improvement of 3.3% and 4.6% on the RTTS and KITTI datasets, respectively. The AOYOLO method effectively enhances the performance of object detection in foggy scenes. Due to its improved performance and stronger robustness, this experimental model provides a new perspective for foggy object detection.
- Object detection in foggy weather,
- vehicle and pedestrian detection,
- YOLOV8 network,
- attention mechanism,
- image enhancement

FullText(HTML)

References(33)

References

[1]	Z. X. Zou, K. Y. Chen, Z. Shi, et al., “Object detection in 20 years: A survey,” Proceedings of the IEEE, vol. 111, no. 3, pp. 257–276, 2023. doi: 10.1109/JPROC.2023.3238524
[2]	Z. Q. Zhao, P. Zheng, S. T. Xu, et al., “Object detection with deep learning: A review,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3212–3232, 2019. doi: 10.1109/TNNLS.2018.2876865
[3]	R. Girshick, “Fast R-CNN,” in Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile, pp. 1440–1448, 2015.
[4]	S. Q. Ren, K. M. He, R. Girshick, et al., “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017. doi: 10.1109/TPAMI.2016.2577031
[5]	J. Redmon, S. Divvala, R. Girshick, et al., “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vegas, NV, USA, pp. 779–788, 2016.
[6]	W. Liu, D. Anguelov, D. Erhan, et al., “SSD: Single shot MultiBox detector,” in Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, pp. 21–37, 2016.
[7]	M. Hassaballah, M. A. Kenk, K. Muhammad, et al., “Vehicle detection and tracking in adverse weather using a deep learning framework,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4230–4242, 2021. doi: 10.1109/TITS.2020.3014013
[8]	Y. Guo, R. L. Liang, Y. K. Cui, et al., “A domain-adaptive method with cycle perceptual consistency adversarial networks for vehicle target detection in foggy weather,” IET Intelligent Transport Systems, vol. 16, no. 7, pp. 971–981, 2022. doi: 10.1049/itr2.12190
[9]	X. Y. Wang and C. Wang, “Vehicle multi-target detection in foggy scene based on foggy env-YOLO algorithm,” in Proceedings of the IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE), Beijing, China, pp. 451–456, 2022.
[10]	M. Li, X. X. Ren, X. B. Hu, et al., “Attention-based radar and camera fusion for object detection in severe conditions,” in Proceedings of the 2022 International Conference on Frontiers of Communications, Information System and Data Science (CISDS), Guangzhou, China, pp. 117–121, 2022.
[11]	C. Sakaridis, D. X. Dai, and L. Van Gool, “Semantic foggy scene understanding with synthetic data,” International Journal of Computer Vision, vol. 126,no,9 pp. 973–992, 2018. doi: 10.1007/s11263-018-1072-8
[12]	W. Y. Liu, G. F. Ren, R. S. Yu, et al., “Image-adaptive YOLO for object detection in adverse weather conditions,” in Proceedings of the 36th AAAI Conference on Artificial Intelligence, virtual event, pp. 1792–1800.
[13]	P. Sen, A. Das, and N. Sahu, “Object detection in foggy weather conditions,” in Proceedings of the 4th International Conference on Intelligent Computing & Optimization, Cham, Germany, pp. 728–737, 2021.
[14]	H. Dong, J. S. Pan, L. Xiang, et al., “Multi-scale boosted dehazing network with dense feature fusion,” in Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 2154–2164, 2020.
[15]	V. A. Sindagi, P. Oza, R. Yasarla, et al., “Prior-based domain adaptive object detection for hazy and rainy conditions,” in Proceedings of the 16th European Conference on Computer Vision, Glasgow, UK, pp. 763–780, 2020.
[16]	J. F. Wang, Y. Chen, Z. K. Dong, et al., “Improved YOLOv5 network for real-time multi-scale traffic sign detection,” Neural Computing and Applications, vol. 35, no. 10, pp. 7853–7865, 2023. doi: 10.1007/s00521-022-08077-5
[17]	B. Y. Li, X. L. Peng, Z. Y. Wang, et al., “AOD-Net: All-in-one dehazing network,” in Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 4780–4788, 2017.
[18]	G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics YOLOv8,” Available at: https://github.com/ultralytics/ultralytics, 2023.
[19]	S. Liu, L. Qi, H. F. Qin, et al., “Path aggregation network for instance segmentation,” in Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Lake City, UT, USA, pp. 8759–8768, 2018.
[20]	M. Y. Ju, C. Ding, W. Q. Ren, et al., “IDE: Image Dehazing and exposure using an enhanced atmospheric scattering model,” IEEE Transactions on Image Processing, vol. 30 pp. 2180–2192, 2021. doi: 10.1109/TIP.2021.3050643
[21]	K. M. He, X. Y. Zhang, S. Q. Ren, et al., “Deep residual learning for image recognition,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vegas, NV, USA, pp. 770–778, 2016.
[22]	Q. B. Hou, D. Q. Zhou, and J. S. Feng, “Coordinate attention for efficient mobile network design,” in Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, pp. 13708–13717, 2021.
[23]	J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Lake City, UT, USA, pp. 7132–7141, 2018.
[24]	S. H. Gao, M. M. Cheng, K. Zhao, et al., “Res2Net: A new multi-scale backbone architecture,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 2, pp. 652–662, 2021. doi: 10.1109/TPAMI.2019.2938758
[25]	Y. C. Liu, Z. R. Shao, and N. Hoffmann, “Global attention mechanism: Retain information to enhance channel-spatial interactions,” arXiv preprint, arXiv: 2112.05561, 2021.
[26]	Z. H. Zheng, P. Wang, D. W. Ren, et al., “Enhancing geometric factors in model learning and inference for object detection and instance segmentation,” IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 8574–8586, 2022. doi: 10.1109/TCYB.2021.3095305
[27]	Z. H. Zheng, P. Wang, W. Liu, et al., “Distance-IoU loss: Faster and better learning for bounding box regression,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, NY, USA, pp. 12993–13000, 2020.
[28]	B. Y. Li, W. Q. Ren, D. P. Fu, et al., “Benchmarking single-image Dehazing and beyond,” IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 492–505, 2019. doi: 10.1109/TIP.2018.2867951
[29]	G. Jocher, “Ultralytics/yolov5,” Available at: https://github.com/ultralytics/yolov5, 2020.
[30]	C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” in Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, pp. 7464–7475, 2023.
[31]	Z. M. Wang, Z. H. Xue, X. H. Wu, et al., “Multi-scale feature fusion for vehicle detection in haze environment,” Computer Systems Applications, vol. 32, no. 2, pp. 217–225, 2023. doi: 10.15888/j.cnki.csa.008957
[32]	C. Lyu, W. W. Zhang, H. A. Huang, et al., “RTMDet: An empirical study of designing real-time object detectors,” arXiv preprint, arXiv: 2212.07784, 2022.
[33]	Z. Ge, S. T. Liu, F. Wang, et al., “YOLOX: Exceeding YOLO series in 2021,” arXiv preprint, arXiv: 2107.08430, 2021.