Published 2024-08-30
Keywords
- Unmanned driving; Obstacle detection; Faster-RCNN model.
How to Cite
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Intelligent vehicles, driven by advancements in computer technology, sensors, and artificial intelligence, are poised to revolutionize the transportation industry. These vehicles require robust systems for environmental perception and collision avoidance to ensure safety and efficiency. This study proposes an improved Faster-RCNN model, incorporating ResNet50 as the feature extraction network, aimed at enhancing obstacle detection accuracy in autonomous driving scenarios. Evaluated on the VOC2007 dataset, the model demonstrates a 12.15% improvement in average detection accuracy over traditional methods. The results indicate the model's superior performance in detecting various objects such as bicycles, buses, and pedestrians, underscoring its potential for broad application in intelligent vehicle systems.
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