401. 基于改进YOLOv3 网络的卡尔曼社交距离 检测与追踪.
- Author
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焦帅, 吴迎年, 张晶, and 孙乐音
- Abstract
In order to prevent COVID-19 from spreading, it is necessary to maintain a certain social security distance while wearing a mask. Because the existing target detection algorithms have problems such as poor real-time performance, low accuracy and unable to detect small scale, an improved algorithm DPPY (dilated pyramid-pooling with YOLOv3) based on YOLOv3(You Only Look Once version 3) was proposed. Firstly, the dilated convolution was used to participate in the core image processing structure of YOLOv3, and then a dense connection network was introduced to further merge the connections between different layers. On this basis, the spatial pyramid structure was imitated to deal with the size of the input data, and finally these processing results were better correlated with the objects to be tracked and the front and back positions of each other. The Kalman filter tool was selected for better processing. If the pedestrians are too close to each other, the warning will be issued in red to better remind the relevant personnel to pay attention. The results show that DPPY algorithm has faster detection speed and higher detection accuracy than traditional YOLOv3 algorithm. The detection speed reaches 34 frames per second, the average precision (AP) is increased by 9.1%, and the mean average precision (mAP) is increased by 7.8%, 8.2%, and 8.9% in large, medium and tiny target detection, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2022