RCNN-based foreign object detection for securing power transmission lines (RCNN4SPTL)

Zhang, Weishan; Liu, Xia; Yuan, Jiangru; Xu, Liang; Zhou, Jiehan; Liu, Xin

doi:10.1016/j.procs.2019.01.232

Cited by 54 publications

(21 citation statements)

References 8 publications

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“…But this algorithm can not detect the potential dangers around the power lines. [17] uses a CNN model based on RCNN to detect foreign objects. However, the algorithm cannot achieve real-time running and only can detect wire-wound foreign objects, while it is powerless to the potential harm caused by construction machinery.…”

Section: Related Work a Image Online Monitoringmentioning

confidence: 99%

“…However, due to the particularity of the application scenario, only a few studies have been undertaken using CNN in detecting foreign objects to monitor transmission lines [4]. There are some difficulties in simply applying the generic CNN to detect foreign objects around transmission lines [17]. Because most of those collected images do not contain foreign objects, which cannot provide useful characteristics to the CNN model during training.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

et al. 2020

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Image online monitoring technology has been widely used in transmission lines inspection, but the intelligent and efficient foreign object detection still has a gap with the ideal. In this paper, we propose a deep learning method to detect invading foreign objects for power transmission line inspection. Specifically, we design our network based on the regression strategy with oriented bounding boxes to accurately predict spatial location and orientation angle of foreign objects, as well as their categories in cluttered backgrounds. Moreover, an easy yet effective Scale Histogram Matching method is proposed to be applied to the publicly available dataset, allowing useful patterns to be exploited to detect tiny foreign objects during the pretraining procedure and boosting detection performance even with limited annotated samples. Besides, we construct an image dataset that contains common foreign objects in transmission line scenarios to evaluate proposed methods, on which experiment results show our full model achieves accuracy with 88.1% mean Average Precision (mAP). Additionally, the efficient and compact network structure allows our network to run in real-time, which provides possibilities for practical use. INDEX TERMS Image online monitoring technology, power transmission line inspection, deep learning, oriented bounding boxes, scale histogram matching.

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Section: Related Work a Image Online Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

et al. 2020

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“…We first compare YOLOv4 with Faster R-CNN for different training sets, and then show the comparison results by YOLOv4, YOLOv3, and SSD [28] on the training set of Set (1,1). In addition, we conduct the comparison study on our approach and the-state-of-the-art abnormal object detection algorithm of power lines, i.e., RCNN4SPTL [29]. RCNN4SPTL is also a deep learning based framework which explores the region proposal network (RPN) to generate the aspect ratio of region proposals for size alignment, and applies end to end training model to improve the efficiency.…”

Section: A Experiments Setupmentioning

confidence: 99%

Cloud-Edge Fusion Based Abnormal Object Detection of Power Transmission Lines Using Incremental Learning

Zhang

Wang²,

Tong³

et al. 2020

IEEE Access

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“…All of the test images in references [19,20] are images with foreign objects, which is equivalent to artificially removing the interference image without foreign objects before their foreign object detection algorithm detects it.…”

Section: Warning-review Strategymentioning

confidence: 99%

“…For example, the automated inspection of insulator [16], transmission towers [17], and transmission lines [18] based on deep learning have already been carried out. The detection of foreign objects on transmission lines based on Faster-RCNN and YOLO (You Only Look Once) were also studied in [19,20] respectively. However, the foreign object image used for the experiment are images with foreign objects by default, so the algorithm does not have the classification function.…”

Section: Introductionmentioning

confidence: 99%

OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance

et al. 2019

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The global demand for electric power has been greatly increasing because of industrial development and the change in people’s daily life. A lot of overhead transmission lines have been installed to provide reliable power across long distancess. Therefore, research on overhead transmission lines inspection is very important for preventing sudden wide-area outages. In this paper, we propose an Overhead Transmission Line Classifier (OTL-Classifier) based on deep learning techniques to classify images returned by future unmanned maintenance drones or robots. In the proposed model, a binary classifier based on Inception architecture is incorporated with an auxiliary marker algorithm based on ResNet and Faster-RCNN(Faster Regions with Convolutional Neural Networks features). The binary classifier defines images with foreign objects such as balloons and kites as abnormal class, regardless the type, size, and number of the foreign objects in a single image. The auxiliary marker algorithm marks foreign objects in abnormal images, in order to provide additional help for quick location of hidden foreign objects. Our OTL-Classifier model achieves a recall rate of 95% and an error rate of 10.7% in the normal mode, and a recall rate of 100% and an error rate of 35.9% in the Warning–Review mode.

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RCNN-based foreign object detection for securing power transmission lines (RCNN4SPTL)

Cited by 54 publications

References 8 publications

A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

Cloud-Edge Fusion Based Abnormal Object Detection of Power Transmission Lines Using Incremental Learning

OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance

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