| Summary: | In order to solve the problems of slow manual inspection speed and low fault detection accuracy of car seat back parts, this article using Q company's car seat back parts researches and designs a car seat back classification and quality inspection screening system. Firstly, SURF (speeded up robust features) is combined with the CNN (convolutional neural network) to classify three types of car seat backrests: A, B, and C. Then, to establish the spring hook angle detection model of the car seat back to detect the misfitting and omission of the Class A car seat back springs, experimental results showed that the neural network-based car seat back detection method proposed in this paper had a feature point mismatch rate, which is less than 1.5% in the classification and recognition of car seat backs. The recognition rate of the training sample was 100% and that of the test sample was 99.56%. The accuracy rate of detection when inspecting 50 car seat backrests reached 98%, and the test results showed that the system can effectively reduce labor costs and improve the detection efficiency of auto parts. © 2022 Shengnan Sun et al.
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