Deep Learning Techniques for Real-Time Object Detection in Autonomous Vehicles
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Abstract
Autonomous vehicles rely heavily on accurate and real-time object detection systems. This paper investigates the application of deep learning models such as YOLO and CNN-based architectures for detecting objects in dynamic environments. The proposed system is tested on benchmark datasets and real-world scenarios, achieving high detection accuracy with minimal latency. The findings demonstrate the effectiveness of deep learning in enhancing safety and navigation in autonomous driving systems.
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