  Real-time object detection plays a vital role in various computer vision
applications. However, deploying real-time object detectors on
resource-constrained platforms poses challenges due to high computational and
memory requirements. This paper describes a low-bit quantization method to
build a highly efficient one-stage detector, dubbed as Q-YOLO, which can
effectively address the performance degradation problem caused by activation
distribution imbalance in traditional quantized YOLO models. Q-YOLO introduces
a fully end-to-end Post-Training Quantization (PTQ) pipeline with a
well-designed Unilateral Histogram-based (UH) activation quantization scheme,
which determines the maximum truncation values through histogram analysis by
minimizing the Mean Squared Error (MSE) quantization errors. Extensive
experiments on the COCO dataset demonstrate the effectiveness of Q-YOLO,
outperforming other PTQ methods while achieving a more favorable balance
between accuracy and computational cost. This research contributes to advancing
the efficient deployment of object detection models on resource-limited edge
devices, enabling real-time detection with reduced computational and memory
overhead.
