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Jian SU, Fang WANG, Wei ZHUANG, “An Improved YOLOv7-tiny Algorithm for Vehicle and Pedestrian Detection with Occlusion in Autonomous Driving,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–13, 2024 doi: 10.23919/cje.2023.00.256
Citation: Jian SU, Fang WANG, Wei ZHUANG, “An Improved YOLOv7-tiny Algorithm for Vehicle and Pedestrian Detection with Occlusion in Autonomous Driving,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–13, 2024 doi: 10.23919/cje.2023.00.256

An Improved YOLOv7-tiny Algorithm for Vehicle and Pedestrian Detection with Occlusion in Autonomous Driving

doi: 10.23919/cje.2023.00.256
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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)

    Fang WANG received the B.E. degree from Nanjing University of Information Science and Technology, Nanjing, China, in 2022, where she is currently an M.S. candidate with the School of Software. Her research interest is in object detection. (Email: 202212490256@nuist.edu.cn)

    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: 2023-07-01
  • Accepted Date: 2023-12-04
  • Available Online: 2024-02-21
  • Future transportation is advancing in the direction of intelligent transportation systems (ITS), where an essential part is vehicle and pedestrian detection. Due to the complex urban traffic environment, vehicles and pedestrians in road monitoring have different forms of occlusion problems, resulting in the missed detection of objects. We design an improved YOLOv7-tiny algorithm for vehicle and pedestrian detection under occlusion, with the following four main improvements. In order to locate the object more accurately, 1×1 convolution and identity connection are added to the 3×3 convolution, and convolution reparameterization is used to enhance the inference speed of the network model. In view of the complex road background and more interference, the Coordinate Attention was added to the connection part of backbone and neck to enhance the network’s capacity to detect the object and lessen interference from other targets. At the same time, before being sent to the detection head, Global Attention Mechanism is added to improve the accuracy of model detection by capturing three-dimensional features. Considering the issue of imbalanced training samples, we propose Focal CIOU Loss instead of CIOU Loss to become the bounding box regression loss, so that the regression process attention to high-quality anchor boxes. Experiments show that the improved YOLOv7-tiny algorithm achieves 82.2% map@0.5 in PASCAL VOC dataset, which is 2.8% higher than before the improvement. The performance of map@0.5:0.95 is 5.2% better than the previous improvement. The proposed improved algorithm can availably to detect partial occlusion objects.
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