ZOS-Net: A Lightweight RGB-T Object Detection Network with Cross-Modal Relation Enhancement and Selective Target Awareness.
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| Title: | ZOS-Net: A Lightweight RGB-T Object Detection Network with Cross-Modal Relation Enhancement and Selective Target Awareness. |
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| Authors: | Dai, Yudan1 (AUTHOR), Guo, Rui1 (AUTHOR) guorui@njust.edu.cn, Yue, Zixiang1 (AUTHOR) |
| Source: | Remote Sensing. Sep2026, Vol. 18 Issue 18, p3112. 38p. |
| Subjects: | Object recognition (Computer vision), Embedded computer systems |
| Abstract: | Highlights: What are the main findings? ZOS-Net achieves a favorable trade-off between detection accuracy and model complexity on M3FD, FLIR-aligned, and VEDAI, using only 4.74 M parameters and 7.9 G FLOPs on M3FD. The proposed ZCGF, OAGR, and SRTPEM modules improve shallow cross-modal feature representation and enhance the responses of small and weak objects in complex backgrounds. What are the implications of the main findings? The results indicate that selective target-aware enhancement is effective for suppressing redundant background information in lightweight RGB-T object detection. ZOS-Net provides a practical RGB-T detection framework for resource-constrained platforms such as UAVs, edge devices, and low-altitude remote sensing systems. In tasks such as autonomous driving, low-altitude remote sensing, intelligent surveillance, and low-altitude security, RGB-T object detection must maintain stable performance under complex conditions, including illumination variations, thermal-source interference, background clutter, and dense distributions of small objects. Existing multimodal detection methods typically rely on complex attention structures or heavyweight fusion modules, making it difficult to balance detection accuracy, model lightweightness, and deployment efficiency; moreover, modality noise and redundant background information are prone to joint propagation during shallow fusion, weakening the responses of small and weak objects. To address these issues, this paper proposes ZOS-Net, a lightweight RGB-T object detection network that integrates cross-modal relation enhancement and selective target awareness. Specifically, ZOS-Net introduces a Cross-Modal Fine-Grained Gated Fusion module, termed ZCGF, at the shallow P3 stage to enhance reliable complementary information from visible textures and infrared thermal responses; an Object-Aware Relation Enhancement module, termed OAGR, is introduced at the semantic bridging stage from P4 to P3 to generate object-relation priors; and a Selective Relation-Guided Target Perception Enhancement Module, termed SRTPEM, is further employed to perform foreground enhancement and weak background calibration on the final P3 features. Experimental results show that ZOS-Net achieves a favorable balance between detection performance and complexity on the M3FD, FLIR-aligned, and VEDAI datasets. On M3FD, ZOS-Net achieves 88.3% mAP50 and 59.5% mAP50:95 with only 4.74 M parameters and 7.9 G FLOPs; on FLIR-aligned, it achieves 83.5% mAP50 and 46.8% mAP50:95; and on VEDAI, it achieves 74.5% mAP50. These results indicate that the proposed method improves the detection performance of small and weak objects in complex backgrounds and provides a lightweight solution for RGB-T object detection on resource-constrained platforms. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? ZOS-Net achieves a favorable trade-off between detection accuracy and model complexity on M3FD, FLIR-aligned, and VEDAI, using only 4.74 M parameters and 7.9 G FLOPs on M3FD. The proposed ZCGF, OAGR, and SRTPEM modules improve shallow cross-modal feature representation and enhance the responses of small and weak objects in complex backgrounds. What are the implications of the main findings? The results indicate that selective target-aware enhancement is effective for suppressing redundant background information in lightweight RGB-T object detection. ZOS-Net provides a practical RGB-T detection framework for resource-constrained platforms such as UAVs, edge devices, and low-altitude remote sensing systems. In tasks such as autonomous driving, low-altitude remote sensing, intelligent surveillance, and low-altitude security, RGB-T object detection must maintain stable performance under complex conditions, including illumination variations, thermal-source interference, background clutter, and dense distributions of small objects. Existing multimodal detection methods typically rely on complex attention structures or heavyweight fusion modules, making it difficult to balance detection accuracy, model lightweightness, and deployment efficiency; moreover, modality noise and redundant background information are prone to joint propagation during shallow fusion, weakening the responses of small and weak objects. To address these issues, this paper proposes ZOS-Net, a lightweight RGB-T object detection network that integrates cross-modal relation enhancement and selective target awareness. Specifically, ZOS-Net introduces a Cross-Modal Fine-Grained Gated Fusion module, termed ZCGF, at the shallow P3 stage to enhance reliable complementary information from visible textures and infrared thermal responses; an Object-Aware Relation Enhancement module, termed OAGR, is introduced at the semantic bridging stage from P4 to P3 to generate object-relation priors; and a Selective Relation-Guided Target Perception Enhancement Module, termed SRTPEM, is further employed to perform foreground enhancement and weak background calibration on the final P3 features. Experimental results show that ZOS-Net achieves a favorable balance between detection performance and complexity on the M3FD, FLIR-aligned, and VEDAI datasets. On M3FD, ZOS-Net achieves 88.3% mAP50 and 59.5% mAP50:95 with only 4.74 M parameters and 7.9 G FLOPs; on FLIR-aligned, it achieves 83.5% mAP50 and 46.8% mAP50:95; and on VEDAI, it achieves 74.5% mAP50. These results indicate that the proposed method improves the detection performance of small and weak objects in complex backgrounds and provides a lightweight solution for RGB-T object detection on resource-constrained platforms. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18183112 |
