FIDC-YOLO: Improved YOLO for Detecting Pine Wilt Disease in UAV Remote Sensing Images via Feature Interaction and Dependency Capturing

Unmanned aerial vehicle (UAV) remote sensing technology has been widely applied in pine wilt disease (PWD) detection. UAV-collected PWD images are characterized by complex backgrounds, small target sizes, and uneven spatial distribution. State-of-the-art generic detection methods typically use Transformers or Mamba-based backbones to mitigate background interference by enhancing the global receptive field. However, directly applying these generic detectors to detect PWD results in suboptimal performance due to insufficient utilization of local features. Although current PWD detection methods use the attention mechanisms to improve the recognition of infected targets, the limitations of Convolutional Neural Networks (CNNs) in capturing long-range dependencies hinder their ability to separate targets from the background. To overcome these challenges, a novel PWD detection method called feature interaction and dependency capturing-YOLO (FIDCYOLO) is proposed in this article. Firstly, to effectively extract the discriminative features of PWD targets, the shuffle efficient layer aggregation network (SELAN) is proposed to promote information interaction between features, improving the model's learning capability. Then, to address the interference of complex backgrounds, the bidirectional extension of Mamba (Hydra) is improved by employing the partial dependency capture (PDC) module, which captures both global and local dependencies for suppressing background information. Additionally, the feature aggregation-spatial pyramid pooling fast (FA-SPPF) module is introduced in front of PDC, which aggregates cross-level features to enhance detection performance. Finally, we use ADown as the downsampling module to avoid the loss of target information. Extensive experiments on our self-constructed PWD dataset and the public DIOR dataset demonstrate the excellent performance of FIDC-YOLO.