Abstract
YOLOv5s is the network with the smallest depth and feature map width and the fastest image inference, but when applied to small pedestrian target detection in complex scenes, the detection still suffers from wrong and missed detections. To address this problem, an improved model based on YOLOv5s is proposed with the addition of a new convolutional neural module, SPD-Conv, which improves the accuracy of the network in detection tasks of low-resolution images or smaller objects. The improved YOLOv5s-SPD model obtained better detection results compared with the original network model, with an average accuracy improvement of 3.9% and an increase in mAP value of about 9.9%.
Publisher
Darcy & Roy Press Co. Ltd.
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