August 7, 2025Journal of Electronic Research and Application0 citationsOpen Access

Research on Image Perception Technology of Autonomous Driving Vehicles Based on Deep Learning

Key Points

Main finding reveals that improvements in autonomous driving image perception can enhance safety and performance.
Key evidence includes analysis of models like faster r-cnn and yolo, showing their limitations in efficiency.
Approach involves proposing enhanced techniques such as data fusion and attention mechanisms to optimize models.
Significance lies in advancing image perception technology, crucial for the development of reliable autonomous vehicles.

Abstract

This paper introduces autonomous driving image perception technology, including deep learning models (such as CNN and RNN) and their applications, analyzing the limitations of traditional algorithms. It elaborates on the shortcomings of Faster R-CNN and YOLO series models, proposes various improvement techniques such as data fusion, attention mechanisms, and model compression, and introduces relevant datasets, evaluation metrics, and testing frameworks to demonstrate the advantages of the improved models.

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