A Lightweight Wildfire Detection Method for Transmission Line Perimeters

Abstract

Due to extreme weather conditions and complex geographical features, the environments around power lines in forest areas have a high risk of wildfires. Once a wildfire occurs, it causes severe damage to the forest ecosystem. Monitoring wildfires around power lines in forested regions through deep learning can reduce the harm of wildfires to natural environments. To address the challenges of wildfire detection around power lines in forested areas, such as interference from complex environments, difficulty detecting small target objects, and high model complexity, a lightweight wildfire detection model based on the improved YOLOv8 is proposed. Firstly, we enhanced the image-feature-extraction capability using a novel feature-extraction network, GS-HGNetV2, and replaced the conventional convolutions with a Ghost Convolution (GhostConv) to reduce the model parameters. Secondly, the use of the RepViTBlock to replace the original Bottleneck in C2f enhanced the model’s feature-fusion capability, thereby improving the recognition accuracy for small target objects. Lastly, we designed a Resource-friendly Convolutional Detection Head (RCD), which reduces the model complexity while maintaining accuracy by sharing the parameters. The model’s performance was validated using a dataset of 11,280 images created by merging a custom dataset with the D-Fire data for monitoring wildfires near power lines. In comparison to YOLOv8, our model saw an improvement of 3.1% in the recall rate and 1.1% in the average precision. Simultaneously, the number of parameters and computational complexity decreased by 54.86% and 39.16%, respectively. The model is more appropriate for deployment on edge devices with limited computational power.