STL-YOLO: A Lightweight Network for External Fire Source Detection in Mining Operations

Small, weak flames can blend into warm-colored backgrounds and be missed during externally caused fires in underground coal mines. Limited computing power also makes it hard to balance detection accuracy and model complexity. This study proposes STL-YOLO, a lightweight fire source detection network based on YOLOv11n, to address these problems. First, the backbone adopts Spatial and Channel Reconstruction Convolution (SCConv) to reduce redundancy in both spatial and channel dimensions and suppress false-positive responses triggered by warm-toned backgrounds. Second, a Texture Contrast Enhancement Module (TCEM) is developed by integrating learnable Sobel texture gradients, parameter-free center-surround local contrast, and multi-kernel contextual gating. TCEM enhances the representation of low-contrast flames and flames obscured by texture similarity, thereby reducing missed detections. Finally, a Lightweight Shared Convolutional Detection head (LSCD) substantially lowers the head’s parameter count while preserving detection accuracy. Experiments on the self-constructed firemix-v6 fire source dataset show that, compared with the YOLOv11n baseline, STL-YOLO improves [email protected] and Recall by 2.5 and 7.0 percentage points, respectively, while reducing the parameter count and theoretical computational cost by 10.12% and 14.29%. These findings demonstrate that STL-YOLO achieves a favorable balance between detection accuracy and model complexity, showing potential for fire source detection on resource-constrained edge platforms such as firefighting robots.