SWVR: A Lightweight Deep Learning Algorithm for Forest Fire Detection and Recognition

Abstract

The timely and effective detection of forest fires is crucial for environmental and socio-economic protection. Existing deep learning models struggle to balance accuracy and a lightweight design. We introduce SWVR, a new lightweight deep learning algorithm. Utilizing the Reparameterization Vision Transformer (RepViT) and Simple Parameter-Free Attention Module (SimAM), SWVR efficiently extracts fire-related features with reduced computational complexity. It features a bi-directional fusion network combining top-down and bottom-up approaches, incorporates lightweight Ghost Shuffle Convolution (GSConv), and uses the Wise Intersection over Union (WIoU) loss function. SWVR achieves 79.6% accuracy in detecting forest fires, which is a 5.9% improvement over the baseline, and operates at 42.7 frames per second. It also reduces the model parameters by 11.8% and the computational cost by 36.5%. Our results demonstrate SWVR’s effectiveness in achieving high accuracy with fewer computational resources, offering practical value for forest fire detection.