FCLGYOLO: Feature Constraint and Local Guided Global Feature for Fire Detection in Unmanned Aerial Vehicle Imagery

Recently, the use of unmanned aerial vehicle (UAV) imagery for object detection in forest fire detection has gained significant attention and has shown remarkable performance. However, most existing object detection models have neglected the exploration of relationships between positive sample features, which is crucial for learning more representative and color-robust features. In addition, small objects in UAV images poses challenges in capturing sufficient object information and hinders accurate object detection. To address these issues, we propose FCLGYOLO that aims to constrain positive sample features and enrich the object information in the feature maps. Specifically, a feature invariance and covariance constraint structure proposed to maintain feature invariance among positive samples and remove internal correlations. Furthermore, a local guided global module proposed to enrich object positioning and semantic information in the feature map, which leverages local features that focus on spatial information to facilitate the learning of global features that focus on frequency information. It is interesting to show that FCLGYOLO performs well even in the presence of heavy smoke or tree occlusions. Compared with multiple state-of-the-art object detection models on a forest fire dataset, experimental results demonstrate the superiority of FCLGYOLO.