Intelligent Vehicle Target Detection based on ARM Processor and Microcontroller

This research presents an intelligent vehicle system using ARM processors and microcontrollers integrated with a lightweight YOLOv5 algorithm for real-time object detection. The system demonstrates significant advancements in embedded vision systems by combining hardware efficiency with deep learning optimization. Utilizing camera inputs, the system efficiently identifies vehicles, pedestrians, and other critical objects in real-time traffic scenarios. The optimized YOLOv5 model ensures smooth performance on embedded platforms with constrained resources while maintaining high detection accuracy. Through comprehensive structural adjustments, pruning, and quantization techniques, we achieve a 40% reduction in model size and a 35% improvement in inference speed compared to baseline implementations. This technology enables intelligent vehicles to quickly respond to environmental changes, effectively supporting intelligent transportation and autonomous driving applications. Experimental results on the KITTI dataset show 95% detection accuracy at 32 FPS, validating the system's practical viability. The integration of ARM processors with microcontroller units creates a heterogeneous computing architecture that optimally balances performance and power consumption. By combining embedded hardware efficiency with advanced deep learning, the system offers a practical and reliable solution for mobile object detection in next-generation intelligent transportation systems.