Task-Aware and Structure-Knowledge-Guided Quantization for End-To-End YOLO Object Detection

Abstract

The YOLO series of models are pivotal for real-time object detection, yet their deployment on resource-constrained edge devices necessitates effective model compression. Post-Training Quantization (PTQ) offers a promising, low-cost solution, but existing methods, primarily designed for classification tasks, often lead to significant performance degradation when applied to YOLO models. In this paper, we systematically analyze the key challenges in quantizing YOLO architectures. We identify three primary obstacles: (1) the high sensitivity of detection tasks to quantization errors, exacerbated by the non-linear IoU metric; (2) the pronounced long-tail distribution of activations, particularly with the SiLU function, which complicates low-bit quantization; and (3) the structural heterogeneity of the multi-scale, multi-task detection head, which renders conventional block-wise quantization strategies ineffective. To address these challenges, we propose a novel framework, Task-Aware and Structure-Knowledge-guided Quantization (TASKQ). Our framework introduces three key components: a sparse quantization strategy to mitigate the impact of long-tailed activations, a Detection-aware Task Regularization (DTR) mechanism that incorporates IoU-based loss to guide parameter fine-tuning, and a Scale-and-Task-Aware Head-wise Quantization (STAHQ) scheme that aligns quantization granularity with the head's functional structure. Extensive experiments on various YOLO models demonstrate that TASKQ significantly outperforms existing PTQ methods, especially in low-bit scenarios, establishing a new state-of-the-art for end-to-end YOLO quantization.