兵工自动化2025，Vol.44Issue(4)：18-25,8.DOI:10.7690/bgzdh.2025.04.005
一种智慧矿山场景下的目标检测算法
A Target Detection Algorithm in Smart Mine Scene
摘要
Abstract
Aiming at the problems of low target pixels,numerous small targets and complex background in the production scene of open-pit mine,an multiscale and super-resolution network(MS_Net)is proposed based on YOLOv5s.In the feature fusion module,the three-scale detection of PANet is upgraded to four-scale detection to improve the multi-scale learning ability of the network,and sub-pixel convolution is used as an up-sampling method;A multi layer fusion(MLF)module is proposed to fuse the features of three output layers of PANet,and a feature map with rich semantic information and spatial information is obtained.In the prediction layer,SIoU is used as the localization loss function to optimize the parameters of the model.The experimental results show that the mAP of MS_Net is 79.4%and the FPS is 59 on PASCAL VOC data set,and the mAP is 80.2%and the FPS is 64.5 on mine data set,and the model can identify and detect the target in the open-pit mine quickly,accurately and efficiently.关键词
智慧矿山/目标检测/YOLOv5s/多层特征融合/子像素卷积Key words
smart mine/target detection/YOLOv5s/multi-layer feature fusion/sub-pixel convolution分类
计算机与自动化引用本文复制引用
姚珊珊,王静宇,郝斌,张飞,高鹭,任晓颖..一种智慧矿山场景下的目标检测算法[J].兵工自动化,2025,44(4):18-25,8.基金项目
内蒙古自治区科技计划项目(2021GG0046 （）
2021GG0048) （）
