Intelligent Identification Method of Insulator Defects Based on CenterMask

Xuan, Zhiming; Ding, Jiwei; Mao, Jing

doi:10.1109/access.2022.3179975

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…The insulator segmentation problem was solved by Antwi-Bekoe et al using a common instance segmentation framework [43], in which the detection and mask branches implemented instance-level segmentation. Xuan et al used a squeeze-excitation module to improve the backbone and a spatial attention module to forecast the insulator mask to produce excellent results in insulator defect segmentation [44].…”

Section: Insulator Segmentationmentioning

confidence: 99%

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Electrical insulator defect detection with incomplete annotations and imbalanced samples

Pang,

Lei,

Zeng

2024

IET Generation Trans & Dist

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Insulators are one of the key components in high‐voltage power systems that prevent transmission lines from grounding. Since they are exposed to different kinds of harsh environments and climates, periodic inspection is indispensable for the safety and high quality of power grid. Nowadays, unmanned aerial vehicle (UAV) inspection is more widely used, facilitating incorporation of convolutional neural network‐based detectors in the insulator detection task. However, these methods are generally based on the assumption that the image samples are balanced among different categories and possess completely ideal annotations. The problem of sample imbalance or incomplete annotation is rarely investigated in depth for insulator defect detection. Here, insulator defect detection with imbalanced data and incomplete annotations is focused on. The proposed framework, named Pi‐index, introduces positive unlabelled (PU) learning to solve the problem of incomplete annotation and designs a novel index the class prior, which is a key parameter in PU learning. Moreover, focal loss is integrated in our framework to alleviate the effect of sample imbalance. Experiment results demonstrate that the proposed framework achieves better performance than the baseline methods in situations of sample imbalance and missing annotation.

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Section: Insulator Segmentationmentioning

confidence: 99%

“…Xuan et al. used a squeeze‐excitation module to improve the backbone and a spatial attention module to forecast the insulator mask to produce excellent results in insulator defect segmentation [44].…”

Section: Related Workmentioning

confidence: 99%

Electrical insulator defect detection with incomplete annotations and imbalanced samples

Pang,

Lei,

Zeng

2024

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…At the end of the framework, the detection and mask branches simultaneously output instance-level bounding box and mask prediction. Xuan et al [36] introduced a modified CenterMask framework to achieve satisfied insulator defect segmentation results, in which the squeezeexcitation module was used to improve the backbone, and the spatial attention module was performed to predict the insulator mask.…”

Section: Insulatormentioning

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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“…On the other hand, single-stage algorithms such as YOLACT [12], Blend Mask [13], and CondInst [14], which perform both localization and segmentation, are quicker and more suitable for the real-time demands of autonomous driving, but they lack accuracy. Additionally, single-stage instance segmentation techniques such as the YOLACT series [12] , SOLO [15] series, and CenterMask [16] can fully utilize positional data in images, resulting in high accuracy and segmentation speed, but their training time is usually lengthy. Although singlestage algorithms typically outperform two-stage algorithms in terms of speed, they often lack the accuracy and precision achieved by two-stage algorithms.…”

Section: Introductionmentioning

confidence: 99%

Improved Convolutional Neural Network for Traffic Scene Segmentation

Xu,

Luo,

Sun

et al. 2024

CMES

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In actual traffic scenarios, precise recognition of traffic participants, such as vehicles and pedestrians, is crucial for intelligent transportation. This study proposes an improved algorithm built on Mask-RCNN to enhance the ability of autonomous driving systems to recognize traffic participants. The algorithm incorporates long and shortterm memory networks and the fused attention module (GSAM, GCT, and Spatial Attention Module) to enhance the algorithm's capability to process both global and local information. Additionally, to increase the network's initial operation stability, the original network activation function was replaced with Gaussian error linear unit. Experiments were conducted using the publicly available Cityscapes dataset. Comparing the test results, it was observed that the revised algorithm outperformed the original algorithm in terms of AP 50 , AP 75 , and other metrics by 8.7% and 9.6% for target detection and 12.5% and 13.3% for segmentation.

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Intelligent Identification Method of Insulator Defects Based on CenterMask

Cited by 7 publications

References 25 publications

Electrical insulator defect detection with incomplete annotations and imbalanced samples

Electrical insulator defect detection with incomplete annotations and imbalanced samples

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

Improved Convolutional Neural Network for Traffic Scene Segmentation

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