CCLNet: An End-to-End Lightweight Network for Small-Target Forest Fire Detection in UAV Imagery

Detecting small forest fire targets in unmanned aerial vehicle (UAV) images is difficult, as flames typically cover only a very limited portion of the visual scene. This study proposes Context-guided Compact Lightweight Network (CCLNet), an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources. CCLNet employs a three-stage network architecture. Its key components include three modules. C3F-Convolutional Gated Linear Unit (C3F-CGLU) performs selective local feature extraction while preserving fine-grained high-frequency flame details. Context-Guided Feature Fusion Module (CGFM) replaces plain concatenation with triplet-attention interactions to emphasize subtle flame patterns. Lightweight Shared Convolution with Separated Batch Normalization Detection (LSCSBD) reduces parameters through separated batch normalization while maintaining scale-specific statistics. We build TF-11K, an 11,139-image dataset combining 9139 self-collected UAV images from subtropical forests and 2000 re-annotated frames from the FLAME dataset. On TF-11K, CCLNet attains 85.8% mAP@0.5, 45.5% mean Average Precision (mAP)@[0.5:0.95], 87.4% precision, and 79.1% recall with 2.21 M parameters and 5.7 Giga Floating-point Operations Per Second (GFLOPs). The ablation study confirms that each module contributes to both accuracy and efficiency. Cross-dataset evaluation on DFS yields 77.5% mAP@0.5 and 42.3% mAP@[0.5:0.95], indicating good generalization to unseen scenes. These results suggest that CCLNet offers a practical balance between accuracy and speed for small-target forest fire monitoring with UAVs.