YOLO-PowerLite V2: An Enhanced Lightweight Detector for Real-Time Tiny Anomaly Identification on Overhead Transmission Lines in Complex Environments.
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| Title: | YOLO-PowerLite V2: An Enhanced Lightweight Detector for Real-Time Tiny Anomaly Identification on Overhead Transmission Lines in Complex Environments. |
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| Authors: | Wei, Shuangfeng1 (AUTHOR), Cai, Yuhang1,2 (AUTHOR), Zhong, Shaobo1,2 (AUTHOR) zhongshaobo@bjast.ac.cn, Lv, Zheng1,2 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1937. 25p. |
| Subjects: | Edge computing, Detectors, Object recognition (Computer vision), Aerial surveillance, Electric lines |
| Abstract: | Highlights: What are the main findings? YOLO-PowerLite V2, built on YOLO11n, integrates C3k2-UIB, MCA, MFM, and MBConv to achieve 0.97 M parameters, 2.8 G FLOPs, and 95.2% mAP@50 for detecting bird nests, defective insulators, and balloons. The proposed model reduces parameters by 62.5% and FLOPs by 56.25% compared to the baseline, while maintaining detection accuracy and outperforming mainstream lightweight detectors. What are the implications of the main findings? The model meets the strict computing constraints of UAV edge devices, enabling real-time tiny anomaly identification for overhead transmission lines in complex environments. It provides a scalable, lightweight design paradigm for customized object detection models in industrial UAV inspection scenarios. Aiming at the core pain point that in existing object detection models, it is difficult to balance detection accuracy and real-time inference efficiency on edge computing devices in UAV-based intelligent inspection of power transmission lines, this paper proposes a lightweight YOLO-PowerLiteV2 model for anomaly target detection in power transmission lines to address the shortcomings of YOLO-PowerLite. Based on YOLO11n as the baseline, the model achieves compression of model volume while guaranteeing detection performance through four core improvements: the C3k2-UIB lightweight backbone module, the MCA (Multi-scale Cross-Axis) attention mechanism, the MBConv lightweight detection head, and the MFM (Modulation Feature Fusion) module. Experiments were conducted on a dataset constructed from 5563 aerial images of transmission lines containing three types of targets: bird nests, defective insulators, and balloons. The results show that YOLO-PowerLiteV2 achieves a mAP@50 of 95.2%, with only 0.97 M parameters and 2.8 G floating point operations (FLOPs). Compared with the baseline model, the number of parameters is reduced by 62.5%, and FLOPs are decreased by 56.25%. On the NVIDIA Jetson Xavier NX edge platform, the model achieves 59.5 FPS with only 16.8 ms latency, outperforming the baseline by 31% in frame rate. Its comprehensive performance outperforms mainstream lightweight detection models. The model demonstrates excellent adaptability to UAV edge-terminal deployment requirements, thereby providing technical support for real-time intelligent inspection of power transmission lines. [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.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194915070 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: YOLO-PowerLite V2: An Enhanced Lightweight Detector for Real-Time Tiny Anomaly Identification on Overhead Transmission Lines in Complex Environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wei%2C+Shuangfeng%22">Wei, Shuangfeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cai%2C+Yuhang%22">Cai, Yuhang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhong%2C+Shaobo%22">Zhong, Shaobo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zhongshaobo@bjast.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Lv%2C+Zheng%22">Lv, Zheng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1937. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors%22">Detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Aerial+surveillance%22">Aerial surveillance</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+lines%22">Electric lines</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? YOLO-PowerLite V2, built on YOLO11n, integrates C3k2-UIB, MCA, MFM, and MBConv to achieve 0.97 M parameters, 2.8 G FLOPs, and 95.2% mAP@50 for detecting bird nests, defective insulators, and balloons. The proposed model reduces parameters by 62.5% and FLOPs by 56.25% compared to the baseline, while maintaining detection accuracy and outperforming mainstream lightweight detectors. What are the implications of the main findings? The model meets the strict computing constraints of UAV edge devices, enabling real-time tiny anomaly identification for overhead transmission lines in complex environments. It provides a scalable, lightweight design paradigm for customized object detection models in industrial UAV inspection scenarios. Aiming at the core pain point that in existing object detection models, it is difficult to balance detection accuracy and real-time inference efficiency on edge computing devices in UAV-based intelligent inspection of power transmission lines, this paper proposes a lightweight YOLO-PowerLiteV2 model for anomaly target detection in power transmission lines to address the shortcomings of YOLO-PowerLite. Based on YOLO11n as the baseline, the model achieves compression of model volume while guaranteeing detection performance through four core improvements: the C3k2-UIB lightweight backbone module, the MCA (Multi-scale Cross-Axis) attention mechanism, the MBConv lightweight detection head, and the MFM (Modulation Feature Fusion) module. Experiments were conducted on a dataset constructed from 5563 aerial images of transmission lines containing three types of targets: bird nests, defective insulators, and balloons. The results show that YOLO-PowerLiteV2 achieves a mAP@50 of 95.2%, with only 0.97 M parameters and 2.8 G floating point operations (FLOPs). Compared with the baseline model, the number of parameters is reduced by 62.5%, and FLOPs are decreased by 56.25%. On the NVIDIA Jetson Xavier NX edge platform, the model achieves 59.5 FPS with only 16.8 ms latency, outperforming the baseline by 31% in frame rate. Its comprehensive performance outperforms mainstream lightweight detection models. The model demonstrates excellent adaptability to UAV edge-terminal deployment requirements, thereby providing technical support for real-time intelligent inspection of power transmission lines. [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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18121937 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1937 Subjects: – SubjectFull: Edge computing Type: general – SubjectFull: Detectors Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Aerial surveillance Type: general – SubjectFull: Electric lines Type: general Titles: – TitleFull: YOLO-PowerLite V2: An Enhanced Lightweight Detector for Real-Time Tiny Anomaly Identification on Overhead Transmission Lines in Complex Environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wei, Shuangfeng – PersonEntity: Name: NameFull: Cai, Yuhang – PersonEntity: Name: NameFull: Zhong, Shaobo – PersonEntity: Name: NameFull: Lv, Zheng IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 12 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
