A dynamic element-activated non-semantic sparse attention method for remote sensing small object detection

Small object detection in remote sensing imagery remains challenging due to complex backgrounds, frequent occlusions, and dense distributions of objects, which often lead to suboptimal performance with existing models. To address these issues, this paper proposes a novel dynamic element-activated non-semantic sparse attention method for detecting small objects in remote sensing images. First, we introduce a non-semantic sparse attention mechanism that computes self-attention within local patches, enhancing the model’s focus on textures and edges while improving its perception of occluded small objects and local complex variations. Subsequently, a dynamic element-activated cross-layer channel attention mechanism is incorporated to adaptively strengthen cross-layer positional awareness, thereby specifically enhancing the representational capacity of small objects feature against cluttered backgrounds. Finally, a diffusion wavelet convolutional structure is employed to process multi-channel features in parallel, mitigating information loss and capturing critical features of densely distributed small objects under boundary ambiguity. Extensive ablation studies and comparisons with state-of-the-art methods on the VisDrone and AI-TODv2 datasets demonstrate the feasibility and effectiveness of our approach, showing its potential to provide technical support for practical applications in remote sensing small object detection.