Small Target Detection Based on Mask-Enhanced Attention Fusion of Visible and Infrared Remote Sensing Images

• URL: http://arxiv.org/abs/2603.06925v1
• Date: Fri, 06 Mar 2026 22:53:27 GMT
• Title: Small Target Detection Based on Mask-Enhanced Attention Fusion of Visible and Infrared Remote Sensing Images
• Abstract summary: This work presents ESM-YOLO+ as a lightweight visible infrared fusion network.<n>To enhance detection, ESM-YOLO+ includes two key innovations.<n>ESM-YOLO+ integrates strong performance with practicality for real-time deployment.
• Score: 8.72869311285096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Targets in remote sensing images are usually small, weakly textured, and easily disturbed by complex backgrounds, challenging high-precision detection with general algorithms. Building on our earlier ESM-YOLO, this work presents ESM-YOLO+ as a lightweight visible infrared fusion network. To enhance detection, ESM-YOLO+ includes two key innovations. (1) A Mask-Enhanced Attention Fusion (MEAF) module fuses features at the pixel level via learnable spatial masks and spatial attention, effectively aligning RGB and infrared features, enhancing small-target representation, and alleviating cross-modal misalignment and scale heterogeneity. (2) Training-time Structural Representation (SR) enhancement provides auxiliary supervision to preserve fine-grained spatial structures during training, boosting feature discriminability without extra inference cost. Extensive experiments on the VEDAI and DroneVehicle datasets validate ESM-YOLO+'s superiority. The model achieves 84.71\% mAP on VEDAI and 74.0\% mAP on DroneVehicle, while greatly reducing model complexity, with 93.6\% fewer parameters and 68.0\% lower GFLOPs than the baseline. These results confirm that ESM-YOLO+ integrates strong performance with practicality for real-time deployment, providing an effective solution for high-performance small-target detection in complex remote sensing scenes.

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