关键词: 行人检测, YOLOV3, 卷积神经网络, 特征图

Abstract: In order to avoid the mutual occlusion of human and objects, the inaccurate detection of small targets, and the influence of complex illumination intensity on pedestrian detection, this paper proposes an improvement of multi-scale clustering convolutional neural network MK-YOLOV3 algorithm to realize the recognition and detection of images. The algorithm improves the YOLOV3. Firstly, the image features are extracted by simple clustering, and the corresponding feature maps are obtained. Then the [K]-means clustering algorithm is combined with the kernel function to determine the anchor position to achieve better clustering. Multi-scale fusion is performed on the shallow feature information of small targets to improve the detection effect of small targets. The simulation results verify that the algorithm has a great improvement on the accuracy and speed of small target recognition on VOC dataset, and has a higher recall rate and accuracy in video intelligence analysis.

Key words: pedestrian detection, YOLOV3, convolutional neural network, feature map