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Maritime Distress Target Detection Based on Improved RT-DETR: For Robust Small Target Localization.

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Title: Maritime Distress Target Detection Based on Improved RT-DETR: For Robust Small Target Localization.
Authors: Liu, Kun1,2 (AUTHOR), Chang, Xinbo2,3 (AUTHOR), Liu, Zhen3 (AUTHOR), Xu, Jian1,4 (AUTHOR), Zhang, Yuhan1,4 (AUTHOR), Liu, Yang1,2 (AUTHOR) y_liu@hrbeu.edu.cn
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p1908. 22p.
Subjects: Object recognition (Computer vision), Loss functions (Statistics), Signal detection, Disaster relief, Drone surveillance
Abstract: Highlights: What are the main findings? An improved RT-DETR-based maritime distress target detection method is proposed, integrating SFConv, SPE module, and Focaler-DIoU loss to significantly enhance small target detection and multi-scale feature representation. The proposed method achieves an mAP@50 of 0.8347, improving detection performance by 4.51% over the baseline while maintaining end-to-end real-time detection capability. What is the implication of the main finding? The method provides a more accurate and robust solution for detecting small and complex maritime distress targets in dynamic ocean environments. It offers practical technical support for real-time UAV-based maritime monitoring and intelligent emergency rescue systems. With the rapid development of maritime transportation and resource development activities, maritime distress events are increasingly frequent, and efficient and accurate target recognition and rescue response methods are urgently needed. The traditional monitoring methods are limited by efficiency and real time, which is difficult to adapt to the complex and changeable marine environment. Therefore, based on the RT-DETR model of transformer architecture, an improved scheme for maritime distress target detection is proposed to improve the small target recognition ability and detection efficiency. Specific improvements include: a small target-focused convolution module (SFConv) is designed to enhance the efficiency of feature extraction and reasoning of small-scale targets; The cross-scale feature interaction optimization module (SPE) is further proposed to improve the ability of multi-scale perception and background suppression; The Focaler-DIoU loss function is introduced to enhance the discrimination performance of the model for difficult samples. On the basis of maintaining the end-to-end detection advantage of RT-DETR, the improvement is of 0.83474, which is 5.7% higher than the original model (0.78964). The accuracy and robustness of the model in complex marine environment is significantly improved, and technical support is provided for the construction of an efficient and intelligent marine monitoring and emergency response system. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Maritime Distress Target Detection Based on Improved RT-DETR: For Robust Small Target Localization.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1908. 22p.
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  Data: Highlights: What are the main findings? An improved RT-DETR-based maritime distress target detection method is proposed, integrating SFConv, SPE module, and Focaler-DIoU loss to significantly enhance small target detection and multi-scale feature representation. The proposed method achieves an mAP@50 of 0.8347, improving detection performance by 4.51% over the baseline while maintaining end-to-end real-time detection capability. What is the implication of the main finding? The method provides a more accurate and robust solution for detecting small and complex maritime distress targets in dynamic ocean environments. It offers practical technical support for real-time UAV-based maritime monitoring and intelligent emergency rescue systems. With the rapid development of maritime transportation and resource development activities, maritime distress events are increasingly frequent, and efficient and accurate target recognition and rescue response methods are urgently needed. The traditional monitoring methods are limited by efficiency and real time, which is difficult to adapt to the complex and changeable marine environment. Therefore, based on the RT-DETR model of transformer architecture, an improved scheme for maritime distress target detection is proposed to improve the small target recognition ability and detection efficiency. Specific improvements include: a small target-focused convolution module (SFConv) is designed to enhance the efficiency of feature extraction and reasoning of small-scale targets; The cross-scale feature interaction optimization module (SPE) is further proposed to improve the ability of multi-scale perception and background suppression; The Focaler-DIoU loss function is introduced to enhance the discrimination performance of the model for difficult samples. On the basis of maintaining the end-to-end detection advantage of RT-DETR, the improvement is of 0.83474, which is 5.7% higher than the original model (0.78964). The accuracy and robustness of the model in complex marine environment is significantly improved, and technical support is provided for the construction of an efficient and intelligent marine monitoring and emergency response system. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Loss functions (Statistics)
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      – SubjectFull: Drone surveillance
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      – TitleFull: Maritime Distress Target Detection Based on Improved RT-DETR: For Robust Small Target Localization.
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              Text: Jun2026
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