Abstract
Pedestrian detection is a technology that uses computer vision to determine whether there are pedestrians passing by in the video sequence or pictures, and realizes the positioning of pedestrians. It is an important task in manless driving, automobile intelligence and intelligent monitoring. Aiming at the problems of low efficiency of pedestrian detection and slow running speed on small devices, a YOLOV6-SE (Yolo Look Only Once-SE) model is constructed to detect pedestrian targets。the mAP detected by pedestrians reaches 85.1%, which is 7.3% points higher than that of the original YOLOv6 without adding attention mechanism. The three-layer channel attention module is added to backbone(RepVGG) adopted by YOLOv6, which enables the backbone network to extract more feature information, thus improving the accuracy of the network. After experimental comparison, Good detection performance is achieved.
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Sun, Z., Chen, B. (2023). Research on Pedestrian Detection and Recognition Based on Improved YOLOv6 Algorithm. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. AIC 2022. Lecture Notes in Electrical Engineering, vol 871. Springer, Singapore. https://doi.org/10.1007/978-981-99-1256-8_33
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DOI: https://doi.org/10.1007/978-981-99-1256-8_33
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