Yolo-inspection: defect detection method for power transmission lines based on enhanced YOLOv5s

Lu, Lihui; Chen, Zhencong; Wang, Rifan; Liu, Li; Chi, Haoqing

doi:10.1007/s11554-023-01360-1

Cited by 11 publications

(2 citation statements)

References 19 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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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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“…Following this, each image is resized to 1000 × 500 × 3 dimensions and then inputted into our proposed XAFCNN model. The XAFCNN model incorporates a combination of Special Attention Module (SAM), Depthwise Separable Convolution (DSC) [26], Separable Convolution (SC) [27], and Convolutional Batch Normalization (CBN) layers [28], working together to effectively extract important local features that are inherent to tire images. The step-by-step construction of the proposed XAFCNN model is illustrated in figure 3.…”

Section: Proposed Modelmentioning

confidence: 99%

Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

Saleh,

Metin ERTUNÇ

2024

Eng. Res. Express

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In the tire manufacturing industry, ensuring the quality of tires is of utmost importance because defective tires have the potential to fail explosively, particularly in high-speed driving situations such as races. To address this issue, thorough visual inspections after production are essential. Nevertheless, detecting defects in tires is a challenging task due to the various textures and structures they have. This paper introduces an Explainable Attention-based Fused Convolutional Neural Network (XAFCNN) model for tire defect detection. Special Attention Module (SAM) is used to prevent overfitting and improve local feature mapping, enabling the detection of small objects. The Grad-CAM heatmap method is utilized to offer significant visual indicators that enrich our comprehension of how the suggested model analyzes data and discerns relevant features within the tire samples. The model was trained on a dataset comprising 38,710 X-ray images of defective tires and 83,985 defect-free tire images, encompassing 15 defect types across 50 design patterns. The results demonstrate the model's exceptional performance, achieving a recall rate of 86.85%, a precision of 98.5%, an F1 score of 92.31%, and an overall accuracy of 95.40%. Notably, the model excels in identifying diverse tire defects and complex textures, making it highly effective in real-world scenarios. By providing both a substantial dataset and a high-performing model, this research advances automated tire defect detection, meeting the industry's imperative for accurate and reliable inspections, and ultimately enhancing human safety.

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DRI-Net: a model for insulator defect detection on transmission lines in rainy backgrounds

Ji,

Gao,

Zhou

et al. 2024

J Real-Time Image Proc

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Yolo-inspection: defect detection method for power transmission lines based on enhanced YOLOv5s

Cited by 11 publications

References 19 publications

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

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

Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

DRI-Net: a model for insulator defect detection on transmission lines in rainy backgrounds

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