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Hybrid Object Detection for Self-Driving Cars by R NITHIYANANDHAM UEE22034 is a document available to read on EtoBox.

What is Hybrid Object Detection for Self-Driving Cars about?

This paper proposes a hybrid object detection approach for self-driving cars that combines the strengths of YOLO and Faster R-CNN to enhance accuracy and speed in real-time environments. The model is trained on a dataset of 10,000 labeled traffic images and demonstrates a 5 to 7 percent accuracy improvement over standalone YOLO models. The findings indicate its potential for practical integration into autonomous driving systems, addressing challenges in object recognition and processing time.

Author

R NITHIYANANDHAM UEE22034

Language

EN