Lightweight CNC Tool Inspection Based on Improved YOLOv8s
Aiming at the use of machine vision technology to detect CNC machining centre tool magazine faults,accompanied by the complexity of the internal environment of the machining centre,the strong back-ground interference and the terminal computational resources are limited and other problems,this paper pro-poses an improved YOLOv8s CNC tool category detection algorithm.Firstly,for the problem of limited ter-minal computing resources,the backbone network is reconstructed,the depth separable convolution is used to replace the backbone network,and the large target monitoring layer of the neck network is eliminated to reduce the amount of model computation;secondly,for the problem of the complex internal environment of machining centres and the strong background interferences,the RepLKCAG module is added at the end of the backbone network,and the global attention mechanism(GAM)is added in the neck network to en-hance the the ability of feature extraction and improve the detection accuracy.Experimental results on home-made datasets show that the improved algorithm reduces the computation amount of the model by 34.15%compared with the YOLOv8s algorithm,improves the accuracy to 96.1%,and increases the mAP50 by 0.5%.
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