LEFE-Net: Lightweight Edge Feature Enhancement for Salient Object Detection in UAV Real-Time Systems

Salient object detection (SOD) is a fundamental component of unmanned aerial vehicle (UAV) vision systems, as it enhances autonomous perception and facilitates precise target localization. However, achieving an optimal balance between model efficiency and detection accuracy remains a significant challenge, particularly in resource-constrained scenarios such as UAVs. To address this issue, a lightweight SOD framework enhanced with edge information is proposed, aiming to achieve both high efficiency and high precision under limited computational resources. First, an edge-aware depthwise separable convolution module is introduced to replace conventional encoder operations, thereby enhancing spatial detail preservation while reducing computational cost. Second, a novel Edge Feature Enhancement Module is designed to extract fine-grained edge features from encoder outputs and to guide the decoder in reconstructing object with higher accuracy. Third, an edge-integrated loss function is developed by incorporating edge supervision into the standard cross-entropy loss, thereby further improving the model’s capability to delineate salient object boundaries. Extensive experiments conducted on multiple benchmark SOD datasets demonstrate that the proposed method achieves competitive, and in some cases superior, performance compared to existing state-of-the-art lightweight models, while maintaining low computational complexity. The proposed framework provides a promising solution for real-time and energy-efficient SOD in low-power computing environments.