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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

AOYOLO Algorithm Oriented Vehicle and Pedestrian Detection in Foggy Weather

doi: 10.23919/cje.2023.00.280
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  • 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
  • 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.
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