Multi-Class Object Detection Using Quantized YOLOv11 for Real-Time Inference

Real-time multi-class object detection on embedded devices poses significant challenges due to limited computational power, memory capacity, and energy efficiency requirements. Conventional high-precision object detectors, such as YOLOv11, deliver outstanding accuracy but are computationally intensive, making them unsuitable for deployment on resource-constrained hardware. This study presents a quantized implementation of the YOLOv11 model designed to enable efficient real-time inference on embedded platforms. The proposed approach applies post-training integer quantization and mixed-precision optimization to minimize computation and memory usage while maintaining detection accuracy across multiple object categories. Experimental evaluations were conducted on the COCO and Pascal VOC datasets. The results indicate that the quantized YOLOv11 achieves a 3.2× increase in inference speed, a 2.7× reduction in memory footprint, and a 35% improvement in energy efficiency, with less than 2% loss in mean Average Precision (mAP) compared to the full-precision baseline. The optimized model sustains real-time performance exceeding 45 frames per second (FPS), demonstrating that quantization is a viable and effective approach for deploying high-performance object detection models on embedded systems.

@article{Soliman2025,
  title     = {Multi-Class Object Detection Using Quantized YOLOv11 for Real-Time Inference},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yehia A. Soliman and Amr Ghoneim and Mahmoud Elkhouly},
  doi       = {10.14569/IJACSA.2025.0161283},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161283}
}