Research on Optimization of Automotive Product Testing Based on Convolutional Neural Networks
With the rapid development of computer vision technology,automatic image recognition and classification in the traditional inspection industry has become possible.By analyzing the performance characteristics of various convolutional neural network models and the work requirements of automotive product testing,this paper constructs and trains a VGGNet model.By adjusting parameters multiple times,the final version of the model is able to accurately identify the vehicle orientation in photos in the record.The accuracy and F1-Score of the model are both as high as 0.95.This work is conducive to promoting the specific application of scientific and innovative technology in the automotive testing industry,and accelerating the realization of"technology empowerment and innovative development"in the industry.
NETL
NSTL
万方数据
