国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Manual detection of bridge cracks is time-consuming and laborious, and the safety is not high. In order to identify and detect bridge cracks efficiently, accurately and without contact, a bridge crack detection model YOLOV5-SA based on improved YOLOv5 is proposed. Based on the YOLOv5s model, firstly, the collected data set is enhanced by geometric transformation and optical transformation. Secondly, Selective Kernel Networks (SKNet, Selective Kernel Networks) were added to the Head part to improve the representation ability of crack features. Finally, on the basis of pyramid Feature notation (FPN), Adaptively Spatial Feature Fusion module was used to strengthen the network feature fusion ability, and to increase the detection of small targets for bridge cracks. The results show that compared with the YOLOv5s model, the improved model can suppress non-critical information better, reduce the interference of invalid information in the background, and improve the accuracy of bridge crack target detection. The accuracy of the improved YOLOv5-SA model reaches 88.1%, which is 1.6% higher than that of the original YOLOv5s model. The average accuracy of mAP0.5 and MAP0.5-0.95 reached 90.0% and 62.1%, respectively, which increased by 2.2% and 2.4%. Compared with other methods related to bridge crack detection (Faster-RCNN, YOLOv4tiny), the proposed YOLOv5-SA model also has comparable or better detection performance. It can be seen that the improved model can detect bridge cracks in complex environments more efficiently, which can provide some ideas for industrial detection.