Real‐time defect detection method based on YOLO‐GSS at the edge end of a transmission line

Hou, Chao; Li, ZhiLei; Shen, XueLiang; Li, GuoChao

doi:10.1049/ipr2.13028

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…To validate the performance of our proposed TL-Yolo, we compare our TL-Yolo with the state-of-the-art methods. The experimental results, from Table 4, show that our TL-Yolo outperforms the Faster R-CNN [39], SPP-Net [40], Yolov5 [41], Yolov7 [42], and Yolov8 [43] networks, which shows its improved detection accuracy and superior efficacy. Table 3 shows that Faster R-CNN and SPP-Net, as two-stage target detection algorithms, are not suitable for real-time target detection due to their high model complexity, large number of parameters and slow reasoning speed.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 94%

TL-YOLO: Foreign-Object Detection on Power Transmission Line Based on Improved Yolov8

Shao,

Zhang,

et al. 2024

Electronics

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Foreign objects on power transmission lines carry a significant risk of triggering large-scale power interruptions which may have serious consequences for daily life if they are not detected and handled in time. To accurately detect foreign objects on power transmission lines, this paper proposes a TL-Yolo method based on the Yolov8 framework. Firstly, we design a full-dimensional dynamic convolution (ODConv) module as a backbone network to enhance the feature extraction capability, thus retaining richer semantic content and important visual features. Secondly, we present a feature fusion framework combining a weighted bidirectional feature pyramid network (BiFPN) and multiscale attention (MSA) module to mitigate the degradation effect of multiscale feature representation in the fusion process, and efficiently capture the high-level feature information and the core visual elements. Thirdly, we utilize a lightweight GSConv cross-stage partial network (GSCSP) to facilitate efficient cross-level feature fusion, significantly reducing the complexity and computation of the model. Finally, we employ the adaptive training sample selection (ATSS) strategy to balance the positive and negative samples, and dynamically adjust the selection process of the training samples according to the current state and performance of the model, thus effectively reducing the object misdetection and omission. The experimental results show that the average detection accuracy of the TL-Yolo method reaches 91.30%, which is 4.20% higher than that of the Yolov8 method. Meanwhile, the precision and recall metrics of our method are 4.64% and 3.53% higher than those of Yolov8. The visualization results also show the superior detection performance of the TL-Yolo algorithm in real scenes. Compared with the state-of-the-art methods, our method achieves higher accuracy and speed in the detection of foreign objects on power transmission lines.

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