EGRN-YOLO: An Enhanced Multi-View Remote Sensing Detection Algorithm for Onshore Wind Turbines Based on YOLOv7
Wind turbines, as the core components of wind power generation systems, play a crucial role in determining the overall generation efficiency and operational safety. However, the challenges posed by complex backgrounds, significant variations in the scale of wind turbine targets, and arbitrary orient...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10910190/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Wind turbines, as the core components of wind power generation systems, play a crucial role in determining the overall generation efficiency and operational safety. However, the challenges posed by complex backgrounds, significant variations in the scale of wind turbine targets, and arbitrary orientations in unmanned aerial vehicle (UAV) remote sensing images have significantly increased the difficulty of real-time wind turbine detection. To address these challenges, this paper introduces an enhanced multi-view onshore wind turbine remote sensing detection algorithm for UAVs based on YOLOv7, termed EGRN-YOLO. Firstly, the lightweight network EfficientNetV2 is utilized as the feature extraction backbone to reduce the number of model parameters and computational load. Secondly, to further minimize the model parameters, the GhostSPPCSPC module is introduced to replace the original pyramid module SPPCSPC. To maintain a balance between accuracy and parameter efficiency, the CAM attention mechanism is integrated to create the GhostSPPCSPC_CAM module, which effectively expands the network’s receptive field and enhances its deep learning capabilities. Subsequently, to improve the network’s focus on small targets and reduce the redundancy of gradient information during neural network inference, the RepNCSPELAN4_KAN module is proposed and integrated into the neck network. Finally, the NWD loss function is introduced to replace the CIoU loss function, thereby improving the accuracy of positive and negative sample allocation for small targets. Experimental results on the self-constructed VOC_WIND_TURBINE dataset demonstrate that the improved EGRN-YOLO model, compared to the benchmark YOLOv7 model, achieves significant improvements of 1.7% in mean average precision (mAP), 3.3% in precision, 1.8% in recall, and 18.1% in inference time. These results indicate a well-balanced trade-off between high accuracy and model efficiency, effectively meeting the demands for identifying wind turbines in complex scenes within remote sensing images. |
|---|---|
| ISSN: | 2169-3536 |