Detection of Tooth Surface Defects Based on Improved YOLOv8n
In order to solve the problems of low detection efficiency and poor detection consistency in manual quality inspection of rough gears,a YOLO-CHD algorithm model based on improved YOLOv8n is proposed.Firstly,the C2f layer is added after the initial convolution of the backbone network to reduce the loss of small target feature de-tails by the subsequent convolutional layer.Secondly,the shallow large-size feature map is further fused in the fea-ture fusion network,and the small target is detected on the higher-resolution feature map with ASFF-4H to im-prove the detection accuracy.Finally,the C2f of the deep feature map in the feature fusion network is replaced by Dual-C2f to further improve the detection accuracy of the model.The experimental results show that the average accuracy of the improved model reaches 73.3%,which is 3.3 percentage points higher than that of the original model,and the inference speed is 42 frames/s,which basically meets the needs of rough machining quality inspec-tion in the tooth surface defect process.
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