A new machine vision detection method for identifying and screening out various large foreign objects on coal belt conveyor lines

Dai, Lili; Zhang, Xu; Gardoni, Paolo; Li, He; Liu, Xinhua; Królczyk, Grzegorz; Li, Zhixiong

doi:10.1007/s40747-023-01011-9

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…During normal delivery, this phenomenon rarely occurs. They typically occur only when the belt seriously deviates or when external sharp objects, such as anchor rods, angle steel, or large gangue, penetrate the belt, potentially causing scratches and tears [145] . Therefore, in line with the cause of the accident, if the foreign object can be accurately detected and removed in the early stage, it can effectively prevent longitudinal tearing to a certain extent and ensure the safe and stable operation of belt transportation [146] .…”

Section: ) Fault Diagnosis Of Belt Conveyor Rollermentioning

confidence: 99%

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

Du,

Zhang,

Liang

et al. 2023

IEEE Access

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The realization of intelligent mining is the only method for realizing high-quality development in the coal industry. As the forefront working link in mine production, achieving automatic roadway tunneling control is key to improving production efficiency, enhancing intelligence, and reducing accident rates at fully mechanized tunneling working faces. Among various detection techniques, machine vision technology stands out with advantages of non-contact measurement, rich information acquisition, and high detection accuracy. The detection and control of tunneling equipment groups based on machine vision has become a research hotspot in the intelligence process of coal mines. This study first introduces the key technologies of a visual detection system, including camera calibration, image preprocessing, feature extraction, visual matching, target segmentation and recognition, visual measurement, and 3D reconstruction. It then elaborates on detection principles, workflows, limitations, precautions, and development status of various vision detection systems in practical application scenarios at tunneling faces, such as tunneling equipments, anchoring systems, transportation systems, and safety auxiliary systems, which significantly improve production safety and efficiency. Finally, considering challenging work conditions and strong interference in mines, the successful adaptation of machine vision to excavation sites relies on addressing technical challenges related to poor environment adaptability, limited imaging field of view, and low intelligence level. Furthermore, according to existing research results and the current technical status, this paper forecasts key technologies that need to be developed in the future for coal mine intelligent equipment systems based on machine vision, including multi-sensor information fusion, equipment group collaborative control, and digital twin-driven remote monitoring.INDEX TERMS Machine vision, coal machinery equipment, detection and measurement, roadway fully mechanized tunneling faces, unmanned coal mine.

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Section: ) Fault Diagnosis Of Belt Conveyor Rollermentioning

confidence: 99%

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

Du,

Zhang,

Liang

et al. 2023

IEEE Access

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“…In previous studies, several researchers have considered machine vision-based evaluation tools for crack length [6], furniture manufacturing process [7], tracking floor eggs [8], grasping control robotic [9], review of surface defect detection [10], screening out conveyor lines [11],…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Machine Vision System for Automatic Dimensioning of Various Shapes based on NI-VBAI

Cheng,

Saputra,

Shi

2024

Proceedings of the 11th International Applied Business and Engineering Conference, ABEC 2023, September 21st, 2023, Bengkalis,

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This paper presents a Real-Time machine vision system based on the NI vision builder for automated inspection (NI-VBAI) platform for automatic dimensions of various shapes. Leveraging advanced machine vision techniques and intelligent NI-VBAI algorithms, the system achieves real-time dimensional accuracy in various forms. Seamless integration with existing industrial setups enables fast and precise measurements without human intervention. Extensive validation and performance evaluation demonstrate the system's robustness, highlighting its potential to revolutionize industrial automation, simplify production processes and ensure timely quality control in manufacturing environments. In this study, we conducted experiments on four objects with two shapes and two sizes. Each measurement process was repeated four times. The processing time for the NI-VBAI experiment can measure an object with 720 ms. The error rate obtained from each measurement process is 1%. Furthermore, the final results show that Ni-VBAI can perform the dimensional measurement process accurately and quickly. The final results can be used directly with configurations in other applications or machines directly for further purposes.

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A low-light image enhancement method for personnel safety monitoring in underground coal mines

Yang,

Wang,

et al. 2024

Complex Intell. Syst.

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Intelligent monitoring technology plays an important role in promoting the development of coal mine safety management. Low illumination in the coal mine underground leads to difficult recognition of monitoring images and poor personnel detection accuracy. To alleviate this problem, a low illuminance image enhancement method proposed for personnel safety monitoring in underground coal mines. Specifically, the local enhancement module maps low illumination to normal illumination at pixel level preserving image details as much as possible. The transformer-based global adjustment module is applied to the locally enhanced images to avoid over-enhancement of bright areas and under-illumination of dark areas, and to prevent possible color deviations in the enhancement process. In addition, a feature similarity loss is proposed to constrain the similarity of target features to avoid the possible detrimental effect of enhancement on detection. Experimental results show that the proposed method improves the detection accuracy by 7.1% on the coal mine underground personal dataset, obtaining the highest accuracy compared to several other methods. The proposed method effectively improves the visualization and detection performance of low-light images, which contributes to the personnel safety monitoring in underground coal mines.

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A new machine vision detection method for identifying and screening out various large foreign objects on coal belt conveyor lines

Cited by 6 publications

References 27 publications

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

A Real-Time Machine Vision System for Automatic Dimensioning of Various Shapes based on NI-VBAI

A low-light image enhancement method for personnel safety monitoring in underground coal mines

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