Research on Optimization of Automobile Product Inspection Based on Convolutional Neural Network

Currently the automotive product inspection industry is facing challenges in image data quality, such as low efficiency in manual verification and inadequate research on technological transformation. In order to improve the efficiency of controlling image data quality and promote the high-quality development of the automotive product inspection industry, this manuscript combines computer vision technology with the basic logic of inspection business. By analyzing the performance characteristics of various convolutional neural network models and the technical requirements of automobile product inspection, an automatic image verification technology framework tailored for automobile product inspection is constructed. Regarding the "car orientation classification" section, a VGGNet model is built and trained, which can accurately identify the orientation of vehicles in sample car verification photos . The model achieved high accuracy and F1- Score of 0.95, confirming the feasibility of the image automatic verification system. This system is conducive to promoting the application of scientific and innovative technologies in the automotive inspection industry, accelerating the industry's "technology empowerment" and "innovative development".