基于改进YOLOv4的行人鞋部检测算法

Detection Algorithm of Pedestrian Shoe Area Based on Improved YOLOv4

结合现场鞋印和周边监控视频锁定犯罪嫌疑人是公安机关刑事侦查破案的一种重要技战法,然而该技战法自动化程度低、耗时耗力,限制了应用范围。针对这一问题,本文提出一种基于YOLOv4算法的目标检测方法,可实现对监控视频下行人鞋部的自动检测。根据行人鞋部区域的特点,首先使用Kmeans聚类算法获得先验框尺度,并确定其数量;然后根据构建的数据集选取合适的检测层以强化对鞋部特征的学习;最后,通过多尺度特征融合,将调整后的空间金字塔池化结构迁移到剪枝后的网络内,增强模型的学习能力。结果表明,提出的YOLOv4_shoe算法训练权重仅为39.56 MB,参数量约为原模型的六分之一,mAP值达到了97.93%,比原YOLOv4模型提升了2.07%。

One of the important tactics used by the public security bureau in a criminal investigation is to combine the related surveillance video and shoeprints on the spot to identify criminal suspects.However,the lowlevel automation of such a method is so laborintensive and timeconsuming,which limits its application.Therefore,this paper proposes an object detection method based on the YOLOv4 algorithm to realize the automatic detection of pedestrian shoes in surveillance video.According to the characteristics of the pedestrian shoe area,first,the Kmeans clustering algorithm is used to determine the scale of the anchor box and confirm its quantity;second,an appropriate detection layer was selected based on the datasets in this paper to improve the learning of shoe features;finally,a multifeature fusion method is used and the adjusted spatial pyramid pooling structure is transferred into the pruned network to improve the learning ability of the model.The experimental results demonstrate that the training weight of the YOLOv4_shoe algorithm proposed is only 39.56 MB,which is approximately onesixth of the original model;and its mean average precision reaches 97.93%,which is 2.07%higher than that of the original YOLOv4 model.