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ARXIV:2603.06925 · REMOTE SENSING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.06925REMOTE SENSINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
ESM-YOLO+ is a lightweight visible-infrared fusion network that enhances small target detection in remote sensing images with mask-enhanced attention fusion and structural representation enhancement.
Opportunity summary
Pain ESM-YOLO+ is a lightweight visible-infrared fusion network that enhances small target detection in remote sensing images with mask-enhanced attention fusion and structural representation enhancement.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
ESM-YOLO+ is a lightweight visible-infrared fusion network that enhances small target detection in remote sensing images with mask-enhanced attention fusion and structural representation enhancement. Building on our earlier ESM-YOLO, this work presents ESM-YOLO+ as…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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…
Remote Sensing moved forward this cycle; last verified April 2026. Public score 7.0/10.
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ESM-YOLO+ is a lightweight visible-infrared fusion network that enhances small target detection in remote sensing images with mask-enhanced attention fusion and structural representation enhancement.
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10.48550/arXiv.2603.06925ESM-YOLO+ is a lightweight visible-infrared fusion network that enhances small target detection in remote sensing images with mask-enhanced attention fusion and structural representation enhancement.
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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PROBLEM
ESM-YOLO+ is a lightweight visible-infrared fusion network that enhances small target detection in remote sensing images with mask-enhanced attention fusion and structural representation enhancement. Building on our earlier ESM-YOLO, this work presents ESM-YOLO+ as a lightweight...
METHOD
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...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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.
WHY NOW
Remote Sensing moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
ESM-YOLO+ is a lightweight visible-infrared fusion network that enhances small target detection in remote sensing images with mask-enhanced attention fusion and structural representation enhancement. Building on our earlier ESM-YOLO, this work presents ESM-YOLO+ as a lightweight visible infrared fusion network.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Remote Sensing moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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ESM-YOLO+ is a lightweight visible-infrared fusion network that enhances small target detection in remote sensing images with mask-enhanced attention fusion and structural representation enhancement.
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Remote Sensing
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