Quantized Object Detection Yolo Model

Abstract

This research establishes a specialized pipeline for real-time object detection in autonomous driving. The work utilizes a diverse automotive dataset to train a model for challenging road conditions. Through mathematical quantization, the project successfully compresses the network for edge hardware. Hardware testing proves that aligning software with dedicated silicon increases speed and efficiency. The findings show that hardware-specific optimisation is critical for stable vehicle AI systems.