Road Surface Pothole Detection Based on YOLOv7-CA-BiFPN
Road potholes are the main road defects of roads,which can damage vehicles,affect driver safety,and even lead to traffic accidents in severe cases.To address this issue,an improved YOLOv7 road pothole detection algorithm is proposed.Mosaic+Mixup is used to to carry out the built-in data augmentation,expand the small sample datasets,and enhance the model generalization ability;By introducing an coordinate attention(CA)attention mechanism,the horizontal and vertical position information is encoded to ensure computational complexity while paying attention to the large-scale position information;BIFPN bidirectional feature pyramid network is adopted to improve detection efficiency through the feature fusion of multi-scale semantic features;By replacing the loss function SIoU with the CIoU,the sample imbalance in regression is effectively solved.Experimental results show that the improved algorithm achieves the mean value and accuracy of 89.42％and 90.12％in pit datasets,which are 6.18％and 1.96％higher than that of the original YOLOv7 version.It can be more accurately and quickly applied to road maintenance.
万方数据
维普
