Surface Anomaly Detection Using Machine Learning Technique
Xiao Weiqi, Steve Teoh Chee Hooi, Siva Raja Sindiramutty, David A L Asirvatham, Devender Kumar, Sahil Verma · 2024
The world is developing rapidly, and the industrial field is gradually reducing labor work and moving to a state of smart work. Machine intelligence operations can often help humans reduce stress in many aspects. Here, we will use related technologies of machine learning. At present, convolutional neural network algorithms are mainly used to apply image-related application scenarios. On this basis, using YOLO technology can achieve faster and more accurate target detection and improve accuracy of detection. Through this improvement, production efficiency can be greatly improved and the stability of the production rhythm can be enhanced. The stability of production rhythm is an extremely important part. It can minimize unnecessary losses and expenditures of enterprises and at the same time obtain greater profits. The improvement of detection accuracy can better protect the health, safety and rights of consumers from a social perspective.