Research on the Optimization of Traffic Sign Recognition Technology based on Deep Learning in Higher Vocational Computer Application Majors

This paper takes the computer application major in higher vocational and technical colleges as the research background and focuses on the application of deep learning - related knowledge in the field of traffic sign recognition. The paper selects images from traffic scene environments, constructs relevant object detection models such as YOLO and Faster R - CNN, and trains and optimizes these models. By drawing charts of the model convergence process and comparing the performance indicators of different models, it elaborates on the performance of different models in traffic sign recognition. The research process and the idea of comparative analysis are applied to the computer application major in higher vocational education, hoping to improve students' practical application ability of deep - learning object detection technology and provide references for industry practitioners and researchers.