A novel multi-target fire smoke segmentation method with imbalance handling for UAV remote sensing images

In the field of early fire and smoke detection using unmanned aerial vehicle (UAV) remote sensing, existing research mainly focuses on single-target segmentation, and there are problems of neglecting the inherent correlation and coexistence between fire and smoke in real scenarios, as well as class imbalance. Furthermore, traditional convolutional neural networks (CNNs) suffer from fixed receptive fields, which make it difficult to balance the detection of large-scale smoke and small-scale flame plumes, as well as insufficient modelling of long-range dependencies, thus resulting in incomplete smoke segmentation. To address these challenges, we propose the Fire and Smoke Segmentation Network FSSNet incorporates an improved lightweight hybrid backbone, integrating ResNet and MobileNetV3, paired with a DeepLabHead-FCNHead decoder to enable high-resolution feature extraction and multi-scale segmentation. A weighted cross-entropy combined loss function is introduced to alleviate data imbalance and enhance performance on small targets. To support validation and address the scarcity of relevant datasets, we have constructed an annotated multi-target dataset encompassing fire, smoke, and background classes. Comprehensive experiments demonstrate that FSSNet outperforms baseline models, FCNHead, DeepLabHead, and LR-ASPPHead, across key metrics. FSSNet achieves a total recall of 84.8%, an MF1 score of 90.3%, and an MIoU of 75.4%, with fire and smoke recall rates exceeding 96.0% and F1 scores of at least 71.4%, indicating a balanced trade-off between precision and recall. This approach enhances the accuracy of collaborative fire and smoke segmentation in UAV remote sensing, and it offers a practical technical solution for early disaster warning. Its hybrid architecture further provides valuable insights for multi-target segmentation in UAV remote sensing. Furthermore, FSSNet maintains lightweight efficiency, thereby meeting the computational constraints of UAV edge devices.