Scratch Detection on the Appearance of Smart Meter HPLC Communication Module Based on Deep Learning - SUN Kai; ZHAI Xiao-hui; ZHAO Ji-fu; SUN Yan-ling; DONG Xian-guang; WANG Yan (Journal of Harbin University of Science and Technology)
Aiming at the problem of the appearance scratch detection of the smart meter HPLC communication module, an advanced pixel-level crack detection architecture Res-DU-Net is proposed, which is characterized by the use of deep convolutional neural network technology. First, Res-DU-Net achieves pixel-level slip detection through operations such as convolution, residual structure, cavity convolution, and cascade, forming a U-shaped model structure. Secondly, the Adam algorithm was used to train and verify the appearance image of the smart meter communication module. Finally, Res-DU-Net has an accuracy rate of 0.985 and a recall rate of 0.987 when the learning rate is 0.001 and the loss rate is 0.020. Experimental results prove that Res-DU-Net has more advantages than traditional methods, Fully Convolutional Network (FCN) and U-Net in pixel-level scratch detection.