Research on Automotive Air Conditioning Vents Defect Detection Based on Im-proved YOLOv5s Algorithm
Aiming at the current problem of low efficiency and high leakage rate of manual labor in the task of de-tecting defects in automotive air conditioning vents,a YOLO-CSD model is constructed to detect them more efficiently.First,coordinate attention(CA)is introduced into the YOLOv5 backbone network to enhance the feature extraction ca-pability of the network for automotive air-conditioning vent defects.Second,the(Space-to-Depth,SPD-Conv)downsam-pling module is introduced to replace the original stepwise convolution and pooling layer in the network to reduce the loss of subtle features in the process.Finally,the coupled detection head is replaced by the decoupled head to allevi-ate the conflict problem between the classification task and the regression task,thus improving the defect detection rate.The average accuracy(mAP)of the improved model reaches 93.6%on the homemade automobile air conditioner vent defect data set,which is 3.4 percentage points higher,and the average detection time per image is 29.4 ms.YOLO-CSD has obvious effects in the detection of defects on automobile air conditioner vents,which lays the founda-tion for the construction of automated quality inspection system for automobile air conditioner vents,and also provides a new reference for surface defects detection of other chrome-plated products.
automotive air vent trim ringsYOLOv5detection of defects on chrome-plated surfacesdeep learning
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