Abstract
As one of the main tasks in the field of computer vision, pedestrian detection aims to find out all pedestrians in the image or video. The existing YOLOv3 is a relatively mature object detection method. However, for the long-distance pedestrian detection task in high-altitude scenes, YOLOv3 has the limitations of low detection speed and low detection accuracy. This paper proposes an improved YOLOv3 method briefly called YOLOv3-M for the high-altitude pedestrian detection, which replaces the feature extraction module called darknet53 in YOLOv3 with MobileNetv1. Specifically, YOLOv3-M first constructs the dataset with the small objects of high-altitude pedestrians as the detection object. Then, it uses the K-means + + algorithm to re-cluster the high-altitude pedestrian dataset. Next, it uses the Distance Intersection over Union (DIoU) loss function to alleviate the problem of high-altitude pedestrian overlapping. Experimental results show that the proposed YOLOv3-M improves the detection precision and the detection speed compared to YOLOv3.
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Acknowledgments
This work is partially supported by National Natural Science Foundation of China (61972187), Natural Science Foundation of Fujian Province (2020J02024), Fuzhou Science and Technology Project (2020-RC-186).
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Tian, Q., Cao, P., Fan, H., Hu, R., Li, Z. (2021). High-Altitude Pedestrian Detection Based on Improved YOLOv3. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_11
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