Sensors, Vol. 24, Pages 2710: Enhanced Lightweight YOLOX for Small Object Wildfire Detection in UAV Imagery

Sensors, Vol. 24, Pages 2710: Enhanced Lightweight YOLOX for Small Object Wildfire Detection in UAV Imagery Sensors doi: 10.3390/s24092710 Authors: Tian Luan Shixiong Zhou Guokang Zhang Zechun Song Jiahui Wu Weijun Pan Target detection technology based on unmanned aerial vehicle (UAV)-derived aerial imagery has been widely applied in the field of forest fire patrol and rescue. However, due to the specificity of UAV platforms, there are still significant issues to be resolved such as severe omission, low detection accuracy, and poor early warning effectiveness. In light of these issues, this paper proposes an improved YOLOX network for the rapid detection of forest fires in images captured by UAVs. Firstly, to enhance the network’s feature-extraction capability in complex fire environments, a multi-level-feature-extraction structure, CSP-ML, is designed to improve the algorithm’s detection accuracy for small-target fire areas. Additionally, a CBAM attention mechanism is embedded in the neck network to reduce interference caused by background noise and irrelevant information. Secondly, an adaptive-feature-extraction module is introduced in the YOLOX network’s feature fusion part to prevent the loss of important feature information during the fusion process, thus enhancing the network’s feature-learning capability. Lastly, the CIoU loss function is used to replace the original loss function, to address iss...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research