| Citation: | QIU Bo, LIU Xiang, SHI Yunyu, et al. A lightweight multi-target real-time detection model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1778-1785. doi: 10.13700/j.bh.1001-5965.2020.0066(in Chinese)
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For the public security monitoring system, a lightweight multi-target real-time detection algorithm is proposed in order to realize the accurate intelligence of the content analysis and improve the actual service ability. First, the multi-fusion gradient cascade structure of CBNet is added based on CenterNet detection network, which effectively solves the problem of insufficient feature extraction capability of the backbone network in daily monitoring videos. Second, the number of parameters is reduced through the model pruning and compression, which can speed up the analysis speed of monitoring videos. During the experiments, the dataset for training and testing consists of a part of COCO datasets and a number of field data collected by ourselves. The ablation experiments are conducted with other mainstream detection algorithms (YOLO, Faster-RCNN, SSD, etc.). The experimental results show that the presented model can effectively balance the speed and precision in the analysis of monitoring videos for public security and has stronger universality.
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