Road target detection in UAV images based on improved YOLOv5 network model
Unmanned aerial vehicle(UAV)remote sensing road images have a chaotic distribution of targets,large size differences,and a large proportion of negative samples.To solve these problems,a road target detection model RA_YOLOv5 in UAV remote sensing images based on YOLOv5X was proposed.Receptive field-coordinate attention convolution was used to replace the conventional convolution kernel in the backbone network,and then the cavity-spatial pyramid pooling-channel attention layer was used to replace the original feature pyramid pooling layer.An adaptive feature fusion layer was introduced in the feature fusion network.Through the weighted fusion of feature maps,the problem of sample and background conflicts between detection maps of different sizes was solved.Decoupled detection heads were employed to calculate regression and classification tasks,respectively,and the loss function was replaced to alleviate the problem of imbalance between positive and negative samples.Experimental results show that RA_YOLOv5 has an average accuracy of 90.42%on the VisDrone data set,which is 7.85%higher than YOLOv5X.The number of detection frames per second in the test environment reaches 35.46 f/s.It can output actual detection results,has good stability,and plays an important role in various scenarios such as road inspection,traffic flow monitoring,and emergency accident handling.