Abstract
Pedestrian detection has extensive applications in computer vision, such as intelligent transportation and security surveillance. YOLOv7 is an object detection model that achieves real-time object detection by dividing the image into grids and predicting bounding boxes and classes for each grid. This study explores and optimizes pedestrian detection based on the YOLOv7 model. Firstly, a large-scale pedestrian dataset is used to train and fine-tune the model to improve detection accuracy and robustness. The YOLOv7 model demonstrates powerful pedestrian detection performance, achieving a high average precision (AP) of 0.92 and a recall rate of 0.85. Experimental results indicate that the pedestrian detection method based on YOLOv7 achieves good results in terms of accuracy and speed, and it has high practical value and application prospects.
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Li, B., Zheng, Q., Tian, X., Elhanashi, A., Saponara, S. (2024). A Pedestrian Detection Method Based on YOLOv7 Model. In: Bellotti, F., et al. Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2023. Lecture Notes in Electrical Engineering, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-031-48121-5_43
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DOI: https://doi.org/10.1007/978-3-031-48121-5_43
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