YOLO-DS: Fine-Grained Feature Decoupling via Dual-Statistic Synergy Operator for Object Detection

2026-01-26

AI Summary

Existing YOLO detectors struggle to effectively model the divergent responses of heterogeneous objects within shared feature channels, limiting performance gains. This work proposes a Dual-Statistic Synergy Operator (DSO), which, for the first time in the YOLO framework, jointly leverages channel-wise mean and peak-to-mean deviation to achieve fine-grained feature decoupling. To enable adaptive feature selection and weighting, we further introduce two lightweight modules: Dual-Statistic Synergy Gating (DSG) and Multi-Path Segmented Gating (MSG). The resulting YOLO-DS, built upon YOLOv8, consistently improves average precision by 1.1%–1.7% across all five scales (N/S/M/L/X) on MS-COCO, with negligible increase in inference latency.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Computer Vision: Object Detection & Categorization
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

One-stage object detection, particularly the YOLO series, strikes a favorable balance between accuracy and efficiency. However, existing YOLO detectors lack explicit modeling of heterogeneous object responses within shared feature channels, which limits further performance gains. To address this, we propose YOLO-DS, a framework built around a novel Dual-Statistic Synergy Operator (DSO). The DSO decouples object features by jointly modeling the channel-wise mean and the peak-to-mean difference. Building upon the DSO, we design two lightweight gating modules: the Dual-Statistic Synergy Gating (DSG) module for adaptive channel-wise feature selection, and the Multi-Path Segmented Gating (MSG) module for depth-wise feature weighting. On the MS-COCO benchmark, YOLO-DS consistently outperforms YOLOv8 across five model scales (N, S, M, L, X), achieving AP gains of 1.1% to 1.7% with only a minimal increase in inference latency. Extensive visualization, ablation, and comparative studies validate the effectiveness of our approach, demonstrating its superior capability in discriminating heterogeneous objects with high efficiency.

Problem

• object detection
• feature decoupling
• heterogeneous objects
• YOLO
• channel-wise modeling

Innovation

• Dual-Statistic Synergy Operator
• Feature Decoupling
• YOLO-DS
• Channel-wise Gating
• Object Detection