An Intelligence Image Processing Method of Visual Servo System in Complex Environment

Li, Shipeng; Li, Di; Zhang, Chunhua; Wang, Juan

doi:10.1109/jsen.2020.3040288

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…Wu et al [11] proposed a leakage detection model based on deep-learning Mask R-CNN, which can detect the leakage area with pixel-level high precision. The deep-learning method gets rid of the dependence on manual feature extraction and realizes a low-cost and high-precision leakage detection method [12], [13], [14]. However, for small early leakage targets with unclear spatial characteristics, the detection effect is not ideal and the anti-interference ability is poor.…”

Section: Introductionmentioning

confidence: 99%

High Reliability Pipeline Leakage Detection Based on Machine Vision in Complex Industrial Environment

Lyu

Zhang

et al. 2022

IEEE Sensors J.

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The vision-based pipeline leakage detection is an intelligent leak detection method based on the industrial Internet of Things monitoring platform. It has the advantages of high safety factor and detection visualization. However, in the actual complex industrial environment, there are some problems, such as environmental interference and transmission quality of sensing networks. These low-quality sensing image data bring noise to the vision-based pipeline leakage detection, resulting in the risk of missed detection and false detection. In view of the above problems, we propose a highly reliable pipeline leakage detection method based on machine vision in the complex industrial environment. First, we propose a key frame selection method based on a lightweight image quality assessment, which can adapt to the complex detection environment. The key frame containing available feature information is selected to eliminate the interference of low-quality sensing image data on subsequent feature extraction and realize pipeline leakage video denoising. Then, the C3D network is used to extract space-time features at the same time to detect the leakage of pipeline leakage video. The experimental results show that the effect of our proposed method is better than other existing methods in the complex industrial environment. When the noisy data ratio of the detected image is 15%, the accuracy can be improved by 2.6 percentage points, up to 97.8%, which ensures the reliability of the pipeline leakage detection in the complex industrial environment.

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Section: Introductionmentioning

confidence: 99%