Lightweight ACG-YOLO with Collaborative Attention Mechanisms for Real-Time Public Safety Surveillance and Edge Deployment

Abstract

Real-time detection of violent acts and hazardous weapons in edge-deployed public safety surveillance remains challenging because resource-constrained devices must balance detection accuracy and computational efficiency, particularly for small-sized weapon targets in complex multi-person interaction scenes. To address these issues, this paper proposes ACG-YOLO, a lightweight collaborative detection framework built upon the YOLOv11n baseline. The model adopts a three-level design involving efficient feature downsampling, spatial localization enhancement, and global context modeling. Specifically, an ADown module reduces information loss during downsampling through a dual-branch structure, helping preserve fine-grained features of small targets; a Coordinate Attention mechanism enhances spatial localization by encoding horizontal and vertical positional information; and a Global Context Enhancement Module (GCEM) incorporates lightweight global contextual information to improve feature representation in complex violent interaction scenes. Experimental results on a violence and weapon detection dataset demonstrate that ACG-YOLO achieves an mAP@0.5 of 89.2%, representing an improvement of 2.9 percentage points over the baseline YOLOv11n, while reducing the parameter count by 19.8% and computational cost by 19.0%, with only 2.07 M parameters and 5.1 GFLOPs. Ablation experiments further examine the contributions of the individual components and their combinations. This research provides a practical solution for real-time early warning and emergency response in resource-constrained public safety surveillance scenarios.