YOLO-GL for efficient multi-target detection in large FOV via hierarchical feature fusion

In modern industrial construction sites, safety inspection tasks face challenges such as large-scale variations and complex multi-object detection scenarios, particularly in detecting critical safety indicators like flames, smoke, personnel attire, and operational behaviors. To address these limitations, this paper proposes YOLO-GL, an enhanced detection network featuring three key innovations: (1) a redesigned Parallelized Local-Global Multi-Level Fusion Module (C2f_gl) with a local-global attention mechanism for improved multi-scale feature representation; (2) a hierarchical feature fusion architecture; and (3) an adaptive feature fusion shuffling module (Multi-Scale Fusion Module and Adaptive Channel Shuffling Module, MSF&ACS) for dynamic optimization of cross-scale semantic relationships. Extensive experiments on composite datasets combining public benchmarks and industrial site collections demonstrate that YOLO-GL achieves state-of-the-art performance, improving mAP@0.5 by 3.5% (from 70.8% to 74.3%) and mAP@0.5:0.95 by 2.9% (from 38.7% to 41.6%) for flame detection, while maintaining real-time processing at 80.59 FPS. The proposed architecture exhibits superior robustness in complex environments, offering an effective solution for industrial safety monitoring applications.