Laptop Appearance Defect Detection Based on Improved YOLOv5 Algorithm

Abstract

During the production of the shell of laptop and during the installation of the laptop its surface may be damaged by external factors. Therefore, its surface quality inspection is an essential and important part of the entire production process. At this stage, the detection of laptop appearance defects within the industry mainly relies on manual inspection, but manual inspection methods are inefficient and costly. In order to reduce the cost of manual labor, realize the intelligence of industrial production as well as improve the efficiency of inspection, in this paper, the YOLOv5 algorithm was used to create a deep learning model to investigate an effective method for detecting scratches defects on the appearance of laptops. In order to speed up the operation of the algorithm and improve the accuracy of the defect detection, the C3 module is used, and the activation function of the Conv module was modified, and the SiLU activation function was used instead of the Hardswish activation function; the experimental results show that the deep learning model trained with the improved YOLOv5 algorithm has a better performance for detecting the scratch defects on the appearance of laptops, not only accelerates the training speed of the model but also achieves an accuracy of 95.0% and a recall of 88%.