Affiliations: [a]
School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China
| [b]
Engineering Research Center of Hubei Province for Clothing Information, Wuhan, China
Abstract: Forest fires can pose a serious threat to the survival of living organisms, and wildfire detection technology can effectively reduce the occurrence of large forest fires and detect them faster. However, the unpredictable and diverse appearance of smoke and fire, as well as interference from objects that resemble smoke and fire, can lead to the overlooking of small objects and detection of false positives that resemble the objects in the detection results. In this work, we propose UAV-FDN, a forest fire detection network based on the perspective of an unmanned aerial vehicle (UAV). It performs real-time wildfire detection of various forest fire scenarios from the perspective of UAVs. The main concepts of the framework are as follows: 1) The framework proposes an efficient attention module that combines channel and spatial dimension information to improve the accuracy and efficiency of model detection under complex backgrounds. 2) It also introduces an improved multi-scale fusion module that enhances the network’s ability to learn objects details and semantic features, thus reducing the chances of small objects being false negative during inspection and false positive issues. 3) Finally, the framework incorporates a multi-head structure and a new loss function, which aid in boosting the network’s updating speed and convergence, enabling better adaptation to different objects scales. Experimental results demonstrate that the UAV-FDN achieves high performance in terms of average precision (AP), precision, recall, and mean average precision (mAP).
