Insulator and Burst Fault Detection Using an Improved Yolov3 Algorithm

Zhou, Fangrong; Peng, Hao; Qian, Guochao; Ma, Yutang; Wen, Gang; Xu, Chao; Kong, Peng; Guobo, Xie; Xiaofeng, Zheng

doi:10.1155/2022/2088937

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…When using the YOLOv3 model to detect insulators on high-altitude high-voltage lines, Zhou et al enhanced the data by rotating angle of the images, and adjusting the exposure and the tone. It not only achieved high precision, but also solves the problem of insufficient data [38]. Chen et al proposed KIND++ low-light image augmentation preprocessing algorithm to improve image quality when solving the problem of coal belt surface defect detection, considering that all data comes from low-light environments.…”

Section: Data Augmentationmentioning

confidence: 99%

Small target detection algorithm based on attention mechanism and data augmentation

Wang,

Liu,

et al. 2024

SIViP

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The detection of masks is of great significance to the prevention of occupational diseases such as infectious diseases and dust diseases. For the problems of small target size, large number of targets, and mutual occlusion in mask-wearing detection, a mask-wearing detection algorithm based on improved YOLOv5s is proposed in present work. First, the ultra-lightweight attention mechanism module ECA was introduced in the Neck layer to improve the accuracy of the model. Second, the influence of different loss functions (GIoU, CIoU, and DIoU) on the improved model was explored, and CIoU was determined as the loss function of the improved model. Besides, the improved model adopted the label smoothing method at the data processing stage, which effectively improved the generalization ability of the model and reduced the risk of overfitting. Finally, the influence of data augmentation methods (Mosaic and Mixup) on model performance was discussed, and the optimal weight of data augmentation was determined. The proposed model was tested on the verification set and the mean precision (mAP), precision, and recall are 92.1%, 90.3%, and 87.4%, respectively. The mAP of the improved algorithm is 4.4% higher than that of the original algorithm.

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Section: Data Augmentationmentioning

confidence: 99%

Small target detection algorithm based on attention mechanism and data augmentation

Wang,

Liu,

et al. 2024

SIViP

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“…YOLOv2 [8,9] has made progress in improving the detection speed and accuracy. YOLOv3 was further improved to support multiple size inputs [10][11][12][13] . YOLOv5 [14][15][16][17][18][19] This research chooses YOLOv8n as the fundamental network for evaluating the quality of diamond particles.…”

Section: Introductionmentioning

confidence: 99%

A study on synthetic diamond quality evaluation using an enhanced YOLOv8n-based algorithm named YOLOv8n_adamas

Zhang,

Li,

ren

et al. 2024

Preprint

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In response to the requirements for synthetic diamond quality evaluation in industrial applications, this study proposes an enhanced algorithm titled YOLOv8n_adamas, derived from an improved variant of the YOLOv8n neural network. To overcome the limitations in feature extraction and recognition of synthetic diamond images using the YOLOv8n model, the backbone architecture was redesigned by referring to ConvNextV2 structure, thereby enhancing the feature extraction capacity of the YOLOv8n backbone. An attention-based dynamic detection head was integrated into the original model, replacing the conventional detection head, leading to an improvement in the model's object detection sensitivity. Experimental results demonstrate that, compared to the baseline YOLOv8n network, the YOLOv8n_adamas algorithm achieves a 3.4% increase in precision, a 3.4% rise in recall, and respective improvements of 2.5% and 1.6% in mean precision at confidence levels of 0.5 and 0.95. Consequently, the accuracy of synthetic diamond quality evaluations has been significantly boosted.

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“…A high-voltage power system consists of high-voltage transmission lines and towers. Insulators are responsible for isolating and preventing the grounding of overhead transmission lines [1,2]. They play a crucial role in maintaining the safety and stabilization of the power grid.…”

Section: Introductionmentioning

confidence: 99%

“…The UAV inspection can automatically capture insulator images, and it enables the image-based insulator detection method. Early investigations of image-based insulator detection rely on hand-crafted features [1,5,6]. The performance of the aforementioned methods depends on the quality of the hand-crafted feature, which is time expensive and requires expert-level experience.…”

Section: Introductionmentioning

confidence: 99%

Pi‐Score: An Estimation Strategy of the Class Prior in Positive‐Unlabeled Learning for Electrical Insulator Defect Detection with Incomplete Annotations

Pang,

Jia,

et al. 2023

Journal of Sensors

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Insulators in high-voltage power systems serve as brackets for overhead lines and prevent these wires from becoming grounded. Due to long-term exposure to a harsh environment, it is indispensable to apply periodic inspection for defective insulators, facilitating the timely overhaul of insulators. In the field of object detection, convolutional neural networks (CNNs) have been introduced and have achieved good performance. Therefore, various CNN-based detectors are applied to the insulator detection task. Because collecting ideal annotations is time-consuming and labor-intensive, research with imperfect annotations has drawn more attention. However, fewer insulator defect detection approaches account for this imperfect annotation problem. This paper focuses on a novel and challenging insulator defect detection scenario: a part of the insulators in datasets is unannotated and viewed as the background. We introduce positive-unlabeled (PU) learning to solve the problem of incomplete annotation for insulator defect detection. To further improve PU learning, a Pi-Score algorithm is proposed to estimate class prior, a crucial parameter in PU loss. Our designed framework is built on Faster R-CNN and incorporates the improved PU learning in the region proposal network. Experimental results on the Insulator Defect Image Dataset (IDID) demonstrate that the proposed framework achieved an average precision (AP) metric that is approximately 1%–2% higher than the positive–negative mainstream detectors with varying degrees of missing annotation. Meanwhile, the proposed framework obtained 0.33 and 0.47 higher AP metrics than the mainstream PU detectors with complete and half of IDID’s annotations.

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Insulator and Burst Fault Detection Using an Improved Yolov3 Algorithm

Cited by 8 publications

References 17 publications

Small target detection algorithm based on attention mechanism and data augmentation

Small target detection algorithm based on attention mechanism and data augmentation

A study on synthetic diamond quality evaluation using an enhanced YOLOv8n-based algorithm named YOLOv8n_adamas

Pi‐Score: An Estimation Strategy of the Class Prior in Positive‐Unlabeled Learning for Electrical Insulator Defect Detection with Incomplete Annotations

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