Research On Metal Surface Defect Detection System Based On Machine Vision

As science and technology advance,the industrial sector,including the field of metal processing,is moving towards modernization and intelligence.Metal surface defect detection is very crucial for metal manufacturing and processing,as it directly affects the quality and efficiency of production.In the past,manufacturers usually relied on traditional manual sampling inspection,which was inefficient and ineffective,and could not meet the actual needs as the production scale expanded.Currently,the deep learning method based on machine vision has become the mainstream in defect detection for its high versatility and flexibility.When it comes to the actual implementation of the deep learning method in metal surface defect detection,following challenges have been found.Firstly,the metal surface is reflective,which interferes with the system performance under different ambient light conditions.As the result,the quality of acquired image becomes worse.Secondly,the metal plate has diverse defects such as tiny defects,large-scale defects and defects which is similar to the background.The various defects demand higher feature extraction ability from the algorithm.The huge number of parameters and computational complexity of sophisticated models require the equipment with sufficient computing power.To solve these problems,this paper proposes a metal surface defect detection system that integrates high-speed image acquisition hardware and excellent deep learning detection algorithm.The research context in this paper is as follows.(1)The paper designs the image acquisition hardware based on FPGA,including the design of ring LED light source,the logic design of OV5640 image sensor,the control of DDR3 high-speed memory and the implementation of Gigabit Ethernet transmission.The design of hardware guarantee the high-quality image data acquisition that supports switching of multiple modes.(2)The paper implements the high-performance model based on YOLOv5l with the optimization of model structure and training methods,such as the improvement of Kmeans++ anchor-box algorithm,the replacement of Mish activation function,the import of ASPPFCSPCG feature extraction module,the adjustment of SGD optimizer and cosine annealing learning-rate strategy,etc.Finally,The final achieves an accuracy of 93.27%,a recall rate of 90.56% and a frame rate of 44.2fps.And this model can effectively detect small-scale and large-scale defects.(3)The paper implements the lightweight model based on YOLOv5 s with several enhancements,such as the replacement of the lightweight Ghost Net backbones,the improvement of the lite RFB feature extraction module and the import of CA attention mechanism.These measures greatly reduce the model’s parameters and computation while improving its ability to distinguish between defects and background.As a result,the model achieves an accuracy of 89.78%,a recall rate of 87.20%,and a frame rate of 238.1fps.Overall,the reduces of computing costs makes it possible to deploy the model on mobile devices and expand the application prospect.(4)The system contains a software interface and is tested in interference and normal situations.The results show that the system is user-friendly and performs well in actual scene,which proves its research and practical value.