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Analysis and Deployment of The Optimal Model for Defect Classification and Target Detection

Effective identification and detection of water-wall tube defects in power plant boiler units are exceptionally signif-icant in reducing the non-stop time of boiler units and avoiding safety accidents. The existing boiler water-wall tube testing technology has some problems, such as dangerous operation, long testing period, high false detection rate, and high testing cost. In this paper, the dataset of boiler water-wall tube defect classification, recognition, and detection is constructed. Then the transfer learning method is adopted to train and compare the classification and recognition model with the target detection model. The optimal model of image defect classification, recognition, and detection are obtained, respectively. Finally, the engineering deployment is carried out. At the same time, we also upload the image classification and recognition module and target detection module to the Web client to display the classification and recognition and detection results of the algorithm server-side in real-time on the Web client. Through the comparative analysis of the experiment, the ResNet101 network is more effective in extracting and learning the defect features of boiler water-wall tubes and better classification and recognition effect on boiler water-wall tubes. The YOLOV5 network has fewer parameters in extracting and learning the target features, which is better than other models.