Research on Automobile Intelligent Manufacturing Defect Detection Algorithm Based on Deep Learning

This research puts forward a deep-learning-centered automotive manufacturing defect detection algorithm. It utilizes the SSD (Single Shot MultiBox Detector) algorithm to realize the efficient detection of surface flaws on automotive components. Initially, the system employs CNN to extract image features and combines multi-scale features for the purpose of strengthening the recognition capability of diverse defects. Subsequently, the model incorporates automatic tagging and data augmentation techniques to enhance the generalization ability of the model with respect to different defect types. This research has designed a range of experimental scenarios and has verified the effectiveness of the algorithm through accurate data analysis. The experimental outcomes indicate that the proposed algorithm has attained a high level in terms of accuracy, recall rate and other metrics. In comparison with traditional detection methods, it exhibits greater robustness and real-time performance. Precisely, in the actual test set, the algorithm has achieved a detection accuracy of 95.6% and a recall rate of 92.8%, which has effectively enhanced the detection efficiency and decreased the false detection rate. The research findings demonstrate that the defect detection algorithm based on deep learning has extensive application prospects in the automotive intelligent manufacturing field, and is anticipated to significantly boost the automation level of the manufacturing process and the reliability of product quality.