Research on Target Detection of Underground Unmanned Vehicle Based on Inverted Residual
Under special working conditions such as dim underground light,uneven lighting,and complex background,the image targets have problems of few detailed features and blurred images.Thus,a detection algorithm based on the inverted residual structure improved YOLOv5s model was proposed to solve the problem of low accuracy in underground target detection.Firstly,the backbone network introduced a Squeeze and Excitation Networks(SE Net)module to improve detection accuracy.The neck network introduced an inverted residual structure in the BottleneckCSP module to expand the channels and enrich the number of features,further improving detection accuracy.The detection experiment was conducted on a self-built underground dataset,and the results show that the average detection accuracy of the model in this paper(intersection over union is 0.5)reached 84.4％,which improved the accuracy of the YOLOv5s model by 16.7 percentage points and reduced the number of parameters by 17.1％.The model in this paper is lightweight and has a high accuracy,which can effectively improve the problem of low accuracy in underground target detection and basically meet the needs of underground unmanned vehicle target detection.
Underground imageTarget detectionInverted residualAttentionYOLOv5s model
万方数据
维普
