Abstract: Deploying deep learning-based object detection models like YOLOv4 on resource-constrained embedded ar-chitectures presents several challenges, particularly regarding computing performance, memory usage, and energy consumption. This study examines the quantization of the YOLOv4 model to facilitate real-time inference on lightweight edge devices, focusing on NVIDIA’s Jetson Nano and AGX. We utilize post-training quantization techniques to reduce both model size and computational complexity, all while striving to maintain acceptable detection accuracy. Experimental results indicate that an 8-bit quantized YOLOv4 model can achieve near real-time performance with minimal accuracy loss. This makes it well-suited for embedded applications such as autonomous navigation. Additionally, this research highlights the trade-offs between model compression and detection performance, proposing an optimization method tailored to the hardware constraints of embedded architectures.

Fatima Zahra Guerrouj, Sergio Rodriiguez Florez, Abdelhafid El Ouardi, Mohamed Abouzahir and Mustapha Ramzi, “Quantized Object Detection for Real-Time Inference on Embedded GPU Architectures” International Journal of Advanced Computer Science and Applications(IJACSA), 16(5), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160503

@article{Guerrouj2025,
title = {Quantized Object Detection for Real-Time Inference on Embedded GPU Architectures},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160503},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160503},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {5},
author = {Fatima Zahra Guerrouj and Sergio Rodriiguez Florez and Abdelhafid El Ouardi and Mohamed Abouzahir and Mustapha Ramzi}
}