Power distribution network inspection vision system based on bionic vision image processing

Hao, Fang; Ma, Jieran; Luo, Linhuan; Dang, Weijun; Xue, Yiwei

doi:10.1007/s13198-021-01268-8

Cited by 1 publication

(2 citation statements)

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“…Hao F and his team members proposed an intelligent distribution network detection system based on visual bionic IP and used Kinect depth information as a parameter of the visual bionic model. According to the research outcomes, the system has the potential to improve the efficiency and management level of distribution detection [15]. In summary, many researchers have conducted different research and designs on DL OD and IP, but the applicability of these methods and systems still needs to be improved.…”

Section: Related Workmentioning

confidence: 99%

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Detection of Apparent Defects in HPLC/Dual Mode Portable Sorting Device Based on Deep Learning and Image Processing

Wang,

Li,

Jiang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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As the growth of automated detection technology, traditional manual detection has gradually been replaced. To improve the effectiveness of defect detection, a HPLC/dual mode portable sorting device with deep learning and image processing is raised for apparent defect detection. The product image is segmented using flood filled mean shift method, and defect detection is achieved using Yolo v3 algorithm. An LME2918 chip is the main component of the communication module in the portable device. Based on experimental data, image segmentation accuracy can range from 80% to 100%, and image matching accuracy can range from 85% to 95%. Under the power line carrier mode, the average success rate of apparent defect detection in portable sorting devices can reach 85%, and the change in success rate is not significant, indicating that the detection is relatively stable; Under wireless communication mode, the average success rate can reach 83%, and the change in success rate is relatively obvious, because wireless communication is easily affected by the external environment. The experimental data shows that the defect detection effect of the HPLC/dual mode portable sorting device based on deep learning and image processing meets the design requirements.

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Section: Related Workmentioning

confidence: 99%

“…In equation (15), the confidence error is expressed by e Con ; the j th candidate box in the i th grid has no targets and is expressed by nobj ij I ; the sigmoid probability of its existence is marked by ˆj i C , and the classification error calculation is shown in equation (16).…”

Section: Design Of Add Based On DLmentioning

confidence: 99%