WCA-VFnet: A Dedicated Complex Forest Smoke Fire Detector

Forest fires pose a significant threat to ecosystems, causing extensive damage. The use of low-resolution forest fire imagery introduces high complexity due to its multi-scene, multi-environment, multi-temporal, and multi-angle nature. This approach aims to enhance the model’s generalizability across diverse and intricate fire detection scenarios. While state-of-the-art detection algorithms like YoloX, Deformable DETR, and VarifocalNet have demonstrated remarkable performance in the field of object detection, their effectiveness in detecting forest smoke fires, especially in complex scenarios with small smoke and flame targets, remains limited. To address this issue, we propose WCA-VFnet, an innovative approach that incorporates the Weld C-A component-a method featuring shared convolution and fusion attention. Furthermore, we have curated a distinctive dataset called T-SMOKE, specifically tailored for detecting small-scale, low-resolution forest smoke fires. Our experimental results show that WCA-VFnet achieves a significant improvement of approximately 35% in average precision (AP) for detecting small flame targets compared to Deformable DETR.