An Algorithmic Approach towards Remote Sensing Imagery Data Restoration Using Guided Filters in Real-Time Applications

Singh, Prabhishek; Diwakar, Manoj; Ghosh, Debjani; Vidyarthi, Ankit; Gupta, Deepak; Gupta, Punit

doi:10.1080/07038992.2023.2257323

Cited by 2 publications

(1 citation statement)

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“…It is also the most effective method to judge belt deviation faults by analyzing the position of the conveyor belt edges. In this study, we chose the Canny edge detection algorithm [23] to extract the features of the conveyor belt bilateral edges. The Canny edge detection algorithm can be divided into the following four steps [24]:…”

Section: Belt Edge Feature Extraction (1) Canny Edge Detectionmentioning

confidence: 99%

Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision

Wu,

Wang,

Tian

et al. 2023

Machines

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Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.

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Section: Belt Edge Feature Extraction (1) Canny Edge Detectionmentioning

confidence: 99%