SRW-YOLO: A Detection Model for Environmental Risk Factors During the Grid Construction Phase
With the rapid advancement of UAV-based remote sensing and image recognition techniques, identifying environmental risk factors from aerial imagery has emerged as a focal point in intelligent inspection during the power transmission and distribution projects construction phase. The uneven spatial di...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/15/2576 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849239655739817984 |
|---|---|
| author | Yu Zhao Fei Liu Qiang He Fang Liu Xiaohu Sun Jiyong Zhang |
| author_facet | Yu Zhao Fei Liu Qiang He Fang Liu Xiaohu Sun Jiyong Zhang |
| author_sort | Yu Zhao |
| collection | DOAJ |
| description | With the rapid advancement of UAV-based remote sensing and image recognition techniques, identifying environmental risk factors from aerial imagery has emerged as a focal point in intelligent inspection during the power transmission and distribution projects construction phase. The uneven spatial distribution of risk factors on construction sites, their weak texture signatures, and the inherently multi-scale nature of UAV imagery pose significant detection challenges. To address these issues, we propose a one-stage SRW-YOLO algorithm built upon the YOLOv11 framework. First, a P2-scale shallow feature detection layer is added to capture high-resolution fine details of small targets. Second, we integrate a reparameterized convolution based on channel shuffle (RCS) of a one-shot aggregation (RCS-OSA) module into the backbone and neck’s shallow layers, enhancing feature extraction while significantly reducing inference latency. Finally, a dynamic non-monotonic focusing mechanism WIoU v3 loss function is employed to reweigh low-quality annotations, thereby improving small-object localization accuracy. Experimental results demonstrate that SRW-YOLO achieves an overall precision of 80.6% and mAP of 79.1% on the State Grid dataset, and exhibits similarly superior performance on the VisDrone2019 dataset. Compared with other one-stage detectors, SRW-YOLO delivers markedly higher detection accuracy, offering critical technical support for multi-scale, heterogeneous environmental risk monitoring during the power transmission and distribution projects construction phase, and establishes the theoretical foundation for rapid and accurate inspection using UAV-based intelligent imaging. |
| format | Article |
| id | doaj-art-331da27af2f241eebe07abacf01eaff9 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-331da27af2f241eebe07abacf01eaff92025-08-20T04:00:53ZengMDPI AGRemote Sensing2072-42922025-07-011715257610.3390/rs17152576SRW-YOLO: A Detection Model for Environmental Risk Factors During the Grid Construction PhaseYu Zhao0Fei Liu1Qiang He2Fang Liu3Xiaohu Sun4Jiyong Zhang5School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaState Grid Economic and Technological Research Institute Co., Ltd., Beijing 102200, ChinaState Grid Economic and Technological Research Institute Co., Ltd., Beijing 102200, ChinaWith the rapid advancement of UAV-based remote sensing and image recognition techniques, identifying environmental risk factors from aerial imagery has emerged as a focal point in intelligent inspection during the power transmission and distribution projects construction phase. The uneven spatial distribution of risk factors on construction sites, their weak texture signatures, and the inherently multi-scale nature of UAV imagery pose significant detection challenges. To address these issues, we propose a one-stage SRW-YOLO algorithm built upon the YOLOv11 framework. First, a P2-scale shallow feature detection layer is added to capture high-resolution fine details of small targets. Second, we integrate a reparameterized convolution based on channel shuffle (RCS) of a one-shot aggregation (RCS-OSA) module into the backbone and neck’s shallow layers, enhancing feature extraction while significantly reducing inference latency. Finally, a dynamic non-monotonic focusing mechanism WIoU v3 loss function is employed to reweigh low-quality annotations, thereby improving small-object localization accuracy. Experimental results demonstrate that SRW-YOLO achieves an overall precision of 80.6% and mAP of 79.1% on the State Grid dataset, and exhibits similarly superior performance on the VisDrone2019 dataset. Compared with other one-stage detectors, SRW-YOLO delivers markedly higher detection accuracy, offering critical technical support for multi-scale, heterogeneous environmental risk monitoring during the power transmission and distribution projects construction phase, and establishes the theoretical foundation for rapid and accurate inspection using UAV-based intelligent imaging.https://www.mdpi.com/2072-4292/17/15/2576power transmission and distribution projectsenvironmental riskobject detectionYOLOv11RCS-OSAWise-IoU |
| spellingShingle | Yu Zhao Fei Liu Qiang He Fang Liu Xiaohu Sun Jiyong Zhang SRW-YOLO: A Detection Model for Environmental Risk Factors During the Grid Construction Phase Remote Sensing power transmission and distribution projects environmental risk object detection YOLOv11 RCS-OSA Wise-IoU |
| title | SRW-YOLO: A Detection Model for Environmental Risk Factors During the Grid Construction Phase |
| title_full | SRW-YOLO: A Detection Model for Environmental Risk Factors During the Grid Construction Phase |
| title_fullStr | SRW-YOLO: A Detection Model for Environmental Risk Factors During the Grid Construction Phase |
| title_full_unstemmed | SRW-YOLO: A Detection Model for Environmental Risk Factors During the Grid Construction Phase |
| title_short | SRW-YOLO: A Detection Model for Environmental Risk Factors During the Grid Construction Phase |
| title_sort | srw yolo a detection model for environmental risk factors during the grid construction phase |
| topic | power transmission and distribution projects environmental risk object detection YOLOv11 RCS-OSA Wise-IoU |
| url | https://www.mdpi.com/2072-4292/17/15/2576 |
| work_keys_str_mv | AT yuzhao srwyoloadetectionmodelforenvironmentalriskfactorsduringthegridconstructionphase AT feiliu srwyoloadetectionmodelforenvironmentalriskfactorsduringthegridconstructionphase AT qianghe srwyoloadetectionmodelforenvironmentalriskfactorsduringthegridconstructionphase AT fangliu srwyoloadetectionmodelforenvironmentalriskfactorsduringthegridconstructionphase AT xiaohusun srwyoloadetectionmodelforenvironmentalriskfactorsduringthegridconstructionphase AT jiyongzhang srwyoloadetectionmodelforenvironmentalriskfactorsduringthegridconstructionphase |