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Focused on the defects that the traditional target detection algorithm has low detection precision and is greatly affected by the industrial imaging environment, YOLO algorithm of computer vision is proposed, the features of the image through the deep convolutional neural network are extracted, and the automatic detection function of the weld area is realized. Firstly, the detected image is preprocessed, and the image inversion, k-nearest median filtering, CLAHE image enhancement, gamma image correction and other algorithms are used to improve the contrast of the weld area in the whole picture. Secondly, the data enhancement algorithm is used to increase the number of training samples and diversity. Finally, the YOLO target detection network is trained by using the training samples obtained from the data enhancement, and the performance of the network is evaluated through test samples. In experiments, 500 images of 50 ray-detected image data are used to train the YOLO target detection network, and the test results are tested in a test set consisting of 50 additional images. The experimental results demonstrate that the accuracy of weld inspection is 96%, it is much better than the accuracy of traditional target detection algorithms, and has guiding value for industrial application.

Search method for weld area in water-cooled wall pipe based on YOLO detection network