CEVG-RTNet: A real-time architecture for robust forest fire smoke detection in complex environments.

Abstract

Forest fire smoke detection is crucial for early warning and emergency management, especially under complex environmental conditions such as low contrast, high transparency, background interference, low illumination, occlusion, and overlapping smoke sources. These factors significantly hinder detection accuracy in real-world scenarios. To address these challenges, we propose CEVG-RTNet, a real-time forest fire smoke detection architecture designed to enhance robustness under such complex conditions. CEVG-RTNet incorporates several novel components. The Spatial-Channel Priori Perceptual Convolution (SCPP-Conv) module improves the model's ability to localize smoke and perceive its morphology, even in low-contrast and high-transparency environments. The Hierarchical Residual Feature Alignment (HRFA) module addresses the challenge of multi-scale feature extraction by aligning local and large-scale smoke features through a residual-guided alignment strategy and multi-layer perceptron (MLP)-based aggregation. To further refine dynamic smoke detection, the Dynamic Recursive Feature Enhancement (DRFE) module applies recursive channel adaptive enhancement and cross-channel attention strategies. Additionally, Polygonal-Intersection over Union (PolyIoU) Loss, a novel loss function, is introduced to handle the morphological complexity of smoke regions. The architecture leverages a graph sparse attention mechanism to enhance accuracy without excessive computational cost. Experimental results demonstrate the effectiveness of CEVG-RTNet, with the variant CEVG-RTNet-n achieving 89.1% precision, 82.9% recall, mAP@0.5 of 89%, and mAP@0.5:0.95 of 58.9%. The model operates with 3.04M parameters, 6.6G FLOPs, and 99.42 FPS, showcasing its strong generalization, anti-interference capabilities, and suitability for complex forest fire smoke detection. The source code is available at: https://github.com/CNNanmuzi/CEVG-RTNet.