Object detection of transmission line visual images based on deep convolutional neural network

Zhu-bo, 周筑博 Zhou; Jiao, 高 佼 Gao; Wei, 张 巍 Zhang; Xiao-jing, 王晓婧 Wang; Jing, 张 静 Zhang

doi:10.3788/yjyxs20183304.0317

Cited by 3 publications

(2 citation statements)

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“…Firstly, use the mobile phone scanning photography function to obtain the status image of the hard pressing plate as a research sample, and use the AdaBoost algorithm to select an optimal threshold value from the feature set of the pressing plate image to construct a 200 × With 200 positive samples, a cascade classifier is obtained through a weighted voting mechanism for the best features. Then, LBP operators and HOG features are used as candidate features for hard plate recognition training 10 .…”

Section: Test On Status Recognition Effect Of Protective Hard Pressin...mentioning

confidence: 99%

Design of an intelligent operation and error prevention and control system for secondary voltage plate in substations based on machine vision

Zhang

Ren

et al. 2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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The correctness of the secondary circuit protection plate for the on/off operation is a prerequisite for the correct action of various types of protection. In the context of the intelligence of various power system equipment, the intelligent operation and management of the secondary protection plate in substations is still a blind spot. This article designs an intelligent operation and "dual confirmation" error prevention and control system for the secondary voltage plate in substations based on machine vision artificial intelligence technology, creating a new intelligent operation mode for the secondary system, achieving on-site precise identification, error prevention interlocking, and digital intelligent verification of the on/off status of the secondary hard plate, completely eliminating problems such as false on/off, missed on/off, and effectively improving the verification efficiency of the secondary protection plate in substations to ensure the correctness of the on/off operation.

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Section: Test On Status Recognition Effect Of Protective Hard Pressin...mentioning

confidence: 99%

Design of an intelligent operation and error prevention and control system for secondary voltage plate in substations based on machine vision

Zhang

Ren

et al. 2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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“…In fact, there are already exist a few researches in deep learning based defect detection of electrical equipment. For instance, [17] uses deep CNN for Status Classification of power line insulator and [18] uses deep CNN for detection of transmission line. However, these methods mainly employ the deep CNN to extract the feature and are only designed for specific electrical equipment.…”

Section: Introductionmentioning

confidence: 99%

Defect Detection from UAV Images Based on Region-Based CNNs

Liu

Zhang

et al. 2018

2018 IEEE International Conference on Data Mining Workshops (ICDMW)

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1 Abstract-With the wide applications of Unmanned Aerial Vehicle (UAV) in engineering such as the inspection of the electrical equipment from distance, the demands of efficient object detection algorithms for abundant images acquired by UAV have also been significantly increased in recent years. In computer vision and data mining communities, traditional object detection methods usually train a class-specific learner (e.g., the SVM) based on the low level features to detect the single class of images by sliding a local window. Thus, they may not suit for the UAV images with complex background and multiple kinds of interest objects. Recently, the deep convolutional neural networks (CNNs) have already shown great advances in the object detection and segmentation fields and outperformed many traditional methods which usually been employed in the past decades. In this work, we study the performance of the regionbased CNN for the electrical equipment defect detection by using the UAV images. In order to train the detection model, we collect a UAV images dataset composes of four classes of electrical equipment defects with thousands of annotated labels. Then, based on the region-based faster R-CNN model, we present a multi-class defects detection model for electrical equipment which is more efficient and accurate than traditional single class detection methods. Technically, we have replaced the RoI pooling layer with a similar operation in Tensorflow and promoted the mini-batch to 128 per image in the training procedure. These improvements have slightly increased the speed of detection without any accuracy loss. Therefore, the modified region-based CNN could simultaneously detect multi-class of defects of the electrical devices in nearly real time. Experimental results on the real word electrical equipment images demonstrate that the proposed method achieves better performance than the traditional object detection algorithms in defect detection.

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