ForestFireDetector: Expanding Channel Depth for Fine-Grained Feature Learning in Forest Fire Smoke Detection

Abstract

Wildfire is a pressing global issue that transcends geographic boundaries. Many areas, including China, are trying to cope with the threat of wildfires and manage limited forest resources. Effective forest fire detection is crucial, given its significant implications for ecological balance, social well-being and economic stability. In light of the problems of noise misclassification and manual design of the components in the current forest fire detection model, particularly the limited capability to identify subtle and unnoticeable smoke within intricate forest environments, this paper proposes an improved smoke detection model for forest fires utilizing YOLOv8 as its foundation. We expand the channel depth for fine-grain feature learning and retain more feature information. At the same time, lightweight convolution reduces the parameters of the model. This model enhances detection accuracy for smoke targets of varying scales and surpasses the accuracy of mainstream models. The outcomes of experiments demonstrate that the improved model exhibits superior performance, and the mean average precision is improved by 3.3%. This model significantly enhances the detection ability while also optimizing the neural network to make it more lightweight. These advancements position the model as a promising solution for early-stage forest fire smoke detection.