An Enhanced Algorithm Integrating YOLOv11 and ByteTrack for Small-Object Detection and Tracking in Low-Altitude Remote Sensing Imagery.
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| Title: | An Enhanced Algorithm Integrating YOLOv11 and ByteTrack for Small-Object Detection and Tracking in Low-Altitude Remote Sensing Imagery. |
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| Authors: | Han, Jianfeng1,2,3 (AUTHOR), Sun, Feijie1,2 (AUTHOR), Xu, Zihan1,2,3 (AUTHOR), Song, Lili1,2 (AUTHOR) songlili@imut.edu.cn, Fang, Jiandong1,2 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1547. 30p. |
| Subjects: | Tracking algorithms, Object recognition (Computer vision), Automatic tracking, Drone photography, Real-time computing, Image stabilization |
| Abstract: | Highlights: What are the main findings? The proposed TCYOLO detector integrates enhanced feature modules and cross-scale fusion architecture, achieving superior small target detection in UAV scenarios while maintaining efficiency. We designed the SofByteTrack tracker with sparse optical flow compensation to mitigate UAV motion-induced tracking drift and identity switches in fast-moving scenarios. What are the implications of the main findings? Through feature enhancement mechanisms, cross-scale fusion architecture, and sparse optical flow motion compensation strategies, significantly improves small target detection accuracy and tracking stability. The technical solutions are applicable to UAVs and mobile vision systems, establishing foundations for future research and supporting intelligent development. In vision-based low-altitude unmanned aerial vehicle (UAV) remote sensing, detecting small targets accurately and maintaining stable tracking under fast-motion conditions remain significant challenges. Specifically, small-object detection suffers from low feature representation, while camera motion often induces tracking drift and identity switches. To address these issues, this paper proposes a novel small target detection and tracking algorithm named TCYOLO-SofByteTrack, which integrates an improved YOLOv11 with ByteTrack. The algorithm comprises two core innovative modules: First, the TCYOLO detector is designed by integrating the C3k2-TA feature enhancement module with triplet attention mechanism to achieve cross-dimensional interaction modeling, significantly improving small target feature representation capability and network contextual awareness. A Cross-Scale Feature Fusion Module for UAVs (CCFM-UAV) is constructed to provide precise detection support for small targets at different scales. Second, building upon the ByteTrack framework, the SofByteTrack tracker is designed, which introduces a sparse optical flow-based motion compensation strategy. This strategy estimates and compensates for image displacement caused by UAV motion in real time, ensuring the stability of target bounding boxes under fast-motion conditions, thereby effectively mitigating tracking drift and identity switches. Experimental results demonstrate that the TCYOLO detector achieves a 7.4% improvement in mAP for small target detection compared to the baseline YOLOv11 model. The complete TCYOLO-SofByteTrack tracking algorithm achieves a HOTA score of 45.3%, MOTA of 42.7%, and IDF1 of 57.8%, representing improvements of 4.5%, 5.9%, and 8.0%, respectively, over the baseline methods. Furthermore, the number of successfully tracked targets increased by 37.3%, while identity switches decreased by 23.4%. These results demonstrate the notable advantages of the proposed method in small target detection accuracy, tracking precision, and identity consistency. Its generalization capability is further validated on a custom highway inspection dataset. Moreover, deployment tests on an NVIDIA Jetson Orin NX platform show that, compared to YOLOv11n, the proposed algorithm achieves higher detection accuracy while still meeting real-time processing requirements, highlighting its practical applicability in resource-constrained scenarios. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The proposed TCYOLO detector integrates enhanced feature modules and cross-scale fusion architecture, achieving superior small target detection in UAV scenarios while maintaining efficiency. We designed the SofByteTrack tracker with sparse optical flow compensation to mitigate UAV motion-induced tracking drift and identity switches in fast-moving scenarios. What are the implications of the main findings? Through feature enhancement mechanisms, cross-scale fusion architecture, and sparse optical flow motion compensation strategies, significantly improves small target detection accuracy and tracking stability. The technical solutions are applicable to UAVs and mobile vision systems, establishing foundations for future research and supporting intelligent development. In vision-based low-altitude unmanned aerial vehicle (UAV) remote sensing, detecting small targets accurately and maintaining stable tracking under fast-motion conditions remain significant challenges. Specifically, small-object detection suffers from low feature representation, while camera motion often induces tracking drift and identity switches. To address these issues, this paper proposes a novel small target detection and tracking algorithm named TCYOLO-SofByteTrack, which integrates an improved YOLOv11 with ByteTrack. The algorithm comprises two core innovative modules: First, the TCYOLO detector is designed by integrating the C3k2-TA feature enhancement module with triplet attention mechanism to achieve cross-dimensional interaction modeling, significantly improving small target feature representation capability and network contextual awareness. A Cross-Scale Feature Fusion Module for UAVs (CCFM-UAV) is constructed to provide precise detection support for small targets at different scales. Second, building upon the ByteTrack framework, the SofByteTrack tracker is designed, which introduces a sparse optical flow-based motion compensation strategy. This strategy estimates and compensates for image displacement caused by UAV motion in real time, ensuring the stability of target bounding boxes under fast-motion conditions, thereby effectively mitigating tracking drift and identity switches. Experimental results demonstrate that the TCYOLO detector achieves a 7.4% improvement in mAP for small target detection compared to the baseline YOLOv11 model. The complete TCYOLO-SofByteTrack tracking algorithm achieves a HOTA score of 45.3%, MOTA of 42.7%, and IDF1 of 57.8%, representing improvements of 4.5%, 5.9%, and 8.0%, respectively, over the baseline methods. Furthermore, the number of successfully tracked targets increased by 37.3%, while identity switches decreased by 23.4%. These results demonstrate the notable advantages of the proposed method in small target detection accuracy, tracking precision, and identity consistency. Its generalization capability is further validated on a custom highway inspection dataset. Moreover, deployment tests on an NVIDIA Jetson Orin NX platform show that, compared to YOLOv11n, the proposed algorithm achieves higher detection accuracy while still meeting real-time processing requirements, highlighting its practical applicability in resource-constrained scenarios. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101547 |
