计算机工程与应用2019，Vol.55Issue(20)：128-133,6.DOI:10.3778/j.issn.1002-8331.1901-0318
改进YOLOV3算法在行人识别中的应用
Application of Improved YOLOV3 Algorithm in Pedestrian Identification
摘要
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 improve-ment on the accuracy and speed of small target recognition on VOC dataset, and has a higher recall rate and accuracy in video intelligence analysis.关键词
行人检测/YOLOV3/卷积神经网络/特征图Key words
pedestrian detection/YOLOV3/convolutional neural network/feature map分类
信息技术与安全科学引用本文复制引用
葛雯,史正伟..改进YOLOV3算法在行人识别中的应用[J].计算机工程与应用,2019,55(20):128-133,6.基金项目
国家自然科学基金(No.61671310) （No.61671310）
辽宁省教育厅项目(No.L201603). （No.L201603）
