A New Collaborative Detection Method for Forest Fires Under Degraded Image Conditions

Affected by global climate change and complex environmental factors, the frequency and intensity of forest fires have been rising. Accurate early detection is crucial for disaster mitigation. Traditional methods (e.g., manual monitoring) suffer from low efficiency or limited coverage, while deep learning methods (e.g., YOLO (You Only Look Once), Faster RCNN (Region-based Convolutional Neural Networks)) perform well but are sensitive to degraded images (haze, low light), reducing accuracy. To address blurred smoke features and attenuated flame brightness in degraded images, this paper proposes CoDeF-Net (Collaborative Detection Framework Network), a collaborative detection framework integrating Retinex-BCE (Retinex-based Bright Channel Enhancement) image enhancement with YOLOv11 (You Only Look Once version 11) to improve robustness. Experiments on 1757 real forest fire images show that Retinex-BCE achieves an FSIMC (Full-Reference Image Quality Assessment Metric based on Structural Similarity and Contrast) index of 0.9611 and an LOE (Loss of Edge) value of 254.78, preserving image structure. CoDeF-Net reaches AP@0.5 (Average Precision at Intersection over Union threshold 0.5) of 87.9% (3.8% higher than original YOLOv11), with low missed detection of small flames and enhanced stability in extreme scenarios, providing a feasible solution for forest fire monitoring under degraded images.