Design and modeling a mid infrared Raman laser on silicon-on-insulator

Ahmadi, Mohammad; Bordiu, Loyc; Shi, Wei; LaRochelle, Sophie

doi:10.1109/pn50013.2020.9167012

Cited by 1 publication

(3 citation statements)

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“…In addition, there are methods for defect detection based on the physical characteristics of insulators using additional mechanical instruments, such as density analysis [7], laser instruments [8], and ultrasonic measurement methods [9]. Some researchers use infrared radiation inspection [40] to detect the heating state of the insulator.…”

Section: Related Workmentioning

confidence: 99%

“…Detection methods based on additional equipment are also limited by the complexity and service life of the operating machinery [7][8][9]. Stability in the power system is necessary to protect human activities.…”

Section: Introductionmentioning

confidence: 99%

“…The challenge of deep neural networks is achieving balance between detection speed and resource consumption. Detection methods based on additional equipment are also limited by the complexity and service life of the operating machinery [7][8][9]. Stability in the power system is necessary to protect human activities.…”

mentioning

confidence: 99%

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Missing-Sheds Granularity Estimation of Glass Insulators Using Deep Neural Networks Based on Optical Imaging

Chen

Zhao

2022

Sensors

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Insulator defect detection is an important task in inspecting overhead transmission lines. However, the surrounding environment is complex, and the detection accuracy of traditional image processing algorithms is low. Therefore, insulator defect detection is still mainly performed manually. In order to improve this situation, we proposed an insulator defect detection method called INSU-YOLO based on deep neural networks. Overexposure points in the image will interfere with insulator detection, so we used image augment to reduce noise and extract the edge information of the insulator. Based on an attention mechanism, we introduced a structure called attention-block where the backbone extracts the feature map, and this aims to improve the ability of our method to detect insulators. Insulators have a variety of specifications, and the location and granularity of defects are also different. Therefore, we proposed an adaptive threat estimation method based on the area ratio between the entire insulator and the defect area. In addition, in order to solve the problem of data shortage, we established a dataset called InsuDetSet for model training. Experiments on the InsuDetSet dataset demonstrated that our model outperforms existing state-of-the-art models regarding both the detection box and speed.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%