Abstract
Steel is an important raw material of fluid components. The technological level limitation leads to the surface faults of the steel, thus the key to improving fluid components quality is to diagnose the faults in steel production. The complex shape and small size of steel surface faults result in the low accuracy of the diagnosis, and the large size of the network leads to poor real-time performance. Therefore, aiming at the problems, an improved YOLOV5 is proposed. Firstly, to reduce the feature information loss, coordinate attention is used to improve YOLOV5, thus the diagnosis ability can be improved. Secondly, to further reduce the loss, a new connection is constructed in YOLOV5, and the detection ability can also be further improved. Thirdly, to improve the real-time performance of the fault diagnosis, YOLOV5 is improved by the lightweight method ShuffleNetV2, and its size can be reduced. Lastly, to further improve the accuracy, the cosine annealing with warm restarts algorithm is used to optimize YOLOV5. The dataset of NEU-DET is verified and testified. The results show that improved YOLOV5 can diagnose steel surface faults with high efficiency and accuracy.