Q_YOLOv5m: A Quantization-based Approach for Accelerating Object Detection on Embedded Platforms
Nizal Alshammry, Taoufik S. Saidani, Nasser S. Albalawi, Sami Mohammed Alenezi, Fahd Alhamazani, Sami Aziz Alshammari, Mohammed Aleinzi, Abdulaziz Alanazi, Mahmoud Salaheldin Elsayed · Engineering Technology & Applied Science Research · 2025
The deployment of deep learning models on resource-constrained embedded platforms presents significant challenges due to limited computational power, memory, and energy efficiency. To address this issue, this study proposes a novel quantization method tailored to accelerate object detection using a quantized version of the YOLOv5m model, called Q_YOLOv5m. This method reduces the model's computational complexity and memory footprint, allowing for faster inference and lower power consumption, making it ideal for real-time applications on embedded systems. This approach incorporates advanced weight and activation quantization techniques to balance performance with accuracy, dynamically adjusting precision based on hardware capabilities. The efficacy of Q_YOLOv5m was confirmed, exhibiting substantial enhancements in inference speed and a reduction in model size with negligible loss in object detection accuracy. The findings underscore the capability of Q_YOLOv5m for edge applications, including autonomous vehicles, intelligent surveillance, and IoT-based monitoring systems.