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Foreign object detection algorithm for transmission lines based on improved RT-DETR
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AI
DOI：10.1088/2631-8695/ae2c40.png)
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摘要
En 中文
In order to solve the problems of complex background, variable target scale, and frequent false and missed detections in transmission line foreign object detection, an algorithm based on improved RT-DETR is proposed in this paper. The algorithm enhances the feature extraction capability and background interference suppression by introducing a CRMB module with integrated inverted residual shift module (iRMB) and cascade group attention (CGA). In addition, a SSFF-Slimneck cross-scale feature fusion network is proposed to mitigate the information loss during feature fusion. Focaler-Shape-IoU is adopted as the bounding box loss function to accelerate model convergence, enhance generalisation capability and improve detection performance. The experimental results show that the proposed method improves 3.3% and 2.3% on mAP@50 and mAP@50:95, respectively, while the parameters and computation are reduced by 24.5% and 16.4%, respectively. This indicates that the proposed method achieves higher detection accuracy while reducing the computational complexity, which significantly improves the foreign object detection capability of transmission lines.
Keyword:
transmission line foreign object
RT-DETR
CRMB
SSFF-Slimneck
Focaler-Shape-IoU
期刊
E
IF:
1.6
论文数:
2.1K
被引数:
0
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