Attention-Guided Multitask Convolutional Neural Network for Power Line Parts Detection

Zhang, Hui; Wu, Liuchen; Chen, Yurong; Chen, Ruibo; Kong, Senlin; Wang, Yaonan; Hu, Jianwen; Wub, Jonathan

doi:10.1109/tim.2022.3162615

Cited by 24 publications

(11 citation statements)

References 43 publications

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“…Reference [6] introduced a method that utilizes soft non‐maximum suppression and the ResNet‐101 network for defect detection in transmission line insulators. Reference [7] developed a spatial region attention block and introduced a multitask framework, combining a RPN to create AGMNet for component detection in transmission lines. Reference [8] improved the YOLOv5 algorithm using an end‐to‐end attention mechanism and a bidirectional feature pyramid network to achieve high‐precision identification of vibration dampers and insulators in transmission lines.…”

Section: Introductionmentioning

confidence: 99%

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

Hou,

Li,

Shen

et al. 2024

IET Image Processing

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Combining edge devices with intelligent inspection for transmission lines can fulfill the demand for real‐time defect detection in the field. However, there has been limited research on algorithms suitable for edge devices with low computational power and memory, and the existing research primarily focuses on CPU optimization. To address these issues, this paper proposes a real‐time defect detection method for transmission line endpoints based on YOLO‐GSS (YOLOv8 with Mosaic‐9, G‐GhostNet, S‐FPN, and Spatial Intersection over Union (SIoU) modifications). First, the authors improve the input of the YOLOv8 network using Mosaic‐9 to increase the number of input features in the training phase and enhance algorithm robustness. Next, the authors introduce G‐GhostNet and S‐FPN to enhance the backbone and neck sections while improving inference speed and accuracy. Finally, the authors modify the Complete Intersection over Union loss function of YOLOv8 using SIoU to further improve the detection accuracy. Experimental results demonstrate that compared to the original YOLOv8, the proposed method achieves a 5x increase in inference speed on Nvidia Jetson NX edge devices and a 7.7% improvement in accuracy, meeting the real‐time defect detection requirements for transmission line field inspections.

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Section: Introductionmentioning

confidence: 99%

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

Hou,

Li,

Shen

et al. 2024

IET Image Processing

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show abstract

“…The test results of various stages are finally fused to improve the robustness of the model. In [16], an attention‐guided network based on spatial region attention blocks is proposed to solve the detection of damper, suspension clamps, and abnormal bodies. In [17], based on YOLOv5, a two‐stage detection method for the pin and missing pin is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…FIGURE16 Test images under different severe weather conditions and different data enhancement strategies.…”

mentioning

confidence: 99%

An improved cascade RCNN detection method for key components and defects of transmission lines

Dong,

Zhang,

Xie

et al. 2023

IET Generation Trans & Dist

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Overhead transmission line detection based on deep learning of aerial images taken by UAVs has been widely investigated. Despite its success, it is limited by several factors, including inappropriate evaluation criteria and dramatic scaling of components in the images. To mitigate these issues, a relative mean Average Precision evaluation index is proposed to accurately measure the model's detection performance for smaller objects. A data enhancement strategy including multi‐scale transformation is adopted to alleviate the problem of drastic scaling. The existing Cascade RCNN target detection technology is enhanced by incorporating Swin‐v2 and a balanced feature pyramid to improve feature characterization capabilities, while side‐aware boundary localization is utilized to improve the positioning accuracy of the model. Experimental results demonstrate that the proposed method outperforms state‐of‐the‐art methods on CPLID and achieves 7.8%, 11.8%, and 5.5% higher detection accuracy than the baseline for mAP50, relative small and medium mAP, respectively. Additionally, the paper discusses the influence of adopted data enhancement on the robustness of the model.

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“…At present, the traditional manual inspection method has been gradually replaced by UAVs with greater flexibility and efficiency. In the transmission line inspection by UAV, the detection objects mainly include insulators [3][4][5], insulator self-explosion [6,7], vibration damper [8,9], bird species [10], and other components [11].…”

Section: Introductionmentioning

confidence: 99%

Transmission Line Object Detection Method Based on Label Adaptive Allocation

et al. 2022

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Inspection of the integrality of components and connecting parts is an important task to maintain safe and stable operation of transmission lines. In view of the fact that the scale difference of the auxiliary component in a connecting part is large and the background environment of the object is complex, a one-stage object detection method based on the enhanced real feature information and the label adaptive allocation is proposed in this study. Based on the anchor-free detection algorithm FCOS, this method is optimized by expanding the real feature information of the adjacent feature layer fusion and the semantic information of the deep feature layer, as well as adaptively assigning the label through the idea of pixel-by-pixel detection. In addition, the grading ring image is sliced in original data to improve the proportion of bolts in the dataset, which can clear the appearance features of small objects and reduce the difficulty of detection. Experimental results show that this method can eliminate the background interference in the GT (ground truth) as much as possible in object detection process, and improve the detection accuracy for objects with a narrow shape and small size. The evaluation index AP (average precision) increased by 4.1%. Further improvement of detection accuracy lays a foundation for the realization of efficient real-time patrol inspection.

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Attention-Guided Multitask Convolutional Neural Network for Power Line Parts Detection

Cited by 24 publications

References 43 publications

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

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

An improved cascade RCNN detection method for key components and defects of transmission lines

Transmission Line Object Detection Method Based on Label Adaptive Allocation

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