Image Processing and Recognition System Based on DaVinci Technology for Coal and Gangue

Zhao, Yi Ding; Sun, Ming

doi:10.4028/www.scientific.net/amm.130-134.2107

Cited by 6 publications

(5 citation statements)

References 2 publications

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“…The AP value is obtained by calculating the average of the precision values for each recall value, as shown in Eq. (18)(19)(20)(21).…”

Section: B Comparative Experimentsmentioning

confidence: 99%

“…The maturity of machine vision technology has been gradually applied in various fields and has been successfully applied in the field of belt conveyor. For example, the measurement of coal volume and coal flow on the conveyor belt [11], classification of materials [12], on-line analysis of ore composition [13][14][15][16][17], gangue identification [18][19][20], conveyor belt detection [21][22][23], and so on. Zhang, et al [24] proposed a conveyor belt deviation detection method based on machine vision technology for fast and timely detection of conveyor belt runout status.…”

Section: Introductionmentioning

confidence: 99%

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Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM

Yang,

Chen,

Liu

et al. 2024

IEEE Access

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The belt surface of the mine belt conveyor can cause serious wear under the condition of longterm high-load operation, which can have a negative impact on production, bring economic losses, even endanger personal safety and cause serious production accidents. Manual detection requires a lot of manpower and material resources, and is highly dependent on empirical judgment, which is with low efficiency and security risks. Therefore, in this study, we introduce a new conveyor belt wear detection algorithm Retinex EfficientNet-NAM-YOLOv8 (RENY algorithm) based on deep learning and machine vision technology to replace manual detection, improving detection efficiency and recognition accuracy. The wear degree of belt is reclassified and defined according to the mechanical properties and wear texture characteristics of belt with different wear degrees, and a new special data set for belt wear detection is established. Aiming at the low brightness, high noise and complex working conditions of the underground mine, Gaussian filtering and bilateral filtering are used as the central surround function of the improved Retinex algorithm, and then channel fusion is performed with the image after histogram equalization and adaptive brightness adjustment. The improved Retinex multi-image fusion algorithm is used to preprocess the collected image. EfficientNet has the performance of reasonably allocating the input resolution, network depth, and channel width, and is able to maximise the performance of the network with limited resources. EfficientNet is used to replace Darknet53 of YOLOv8 as the backbone of the feature extraction network, which improves the detection accuracy under limited computing resources. A lightweight attention module NAM is added to the improved network, which improves the detection speed without reducing the detection accuracy. Experimental results show that RENY algorithm effectively maintains the smoothness of the image during the image preprocessing stage, improves the brightness and contrast of the image, and better preserves the edge information of the image. RENY algorithm achieves a detection speed of 66FPS and an average accuracy of 98.57%. Compared with the original YOLOv8 algorithm, the accuracy of RENY algorithm is increased by 6.4% and the speed is increased by 13.2%. In comparison experiments with similar methods, RENY algorithm occupies less hardware resources, has strong generalization ability, good performance, and has high detection speed and accuracy.

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“…The AP value is obtained by calculating the average of the precision values for each recall value, as shown in Eq. (18)(19)(20)(21).…”

Section: B Comparative Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM

Yang,

Chen,

Liu

et al. 2024

IEEE Access

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show abstract

“…Technology in the field of object recognition [4][5] Foreign object detection based on picture recognition has drawn the attention of academics both domestically and internationally. A coal gangue image processing and recognition system based on Davinci technology was proposed by Zhao et al [6] A method for recognizing coal gangue based on image processing was proposed by Li et al [7]. A coal and gangue image recognition method based on a nonlinear greyscale compression expansion co-occurrence matrix was proposed by Le et al [8] The above method uses conventional image recognition technology to detect a piece of foreign object that looks like gangue.…”

Section: With the Outstanding Performance Of Machine Visionmentioning

confidence: 99%

Foreign Object Detection Method of Conveyor Belt Based on Improved Nanodet

2023

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“…Multi-robot system has better performance compared with single robot system, which can complete more complex formation. But in order to meet the capacity of cooperative robotics, it needs to use the wireless communication to exchange data between the host computer and slave computer [1][2]. This paper uses ZigBee wireless communication system to design and test multi-robot communication system, and obtains a new multi-intelligent collaborative network communication system.…”

Section: Introductionmentioning

confidence: 99%

Design of Multi-Intelligent Robot Wireless Communication System Based on ZigBee Automation Control

Chen

Xiong

2014

AMM

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This paper designs a new ZigBee intelligent robot communication control system, which realizes one to many data transmission. Upper computer can send commands to control multiple mobile intelligent robots to do the action response through wireless module. Slave computer can also send data to host computer to achieve remote monitoring, and has high relay ability. In order to verify the effectiveness and reliability of the system, this paper tests the system and obtains 3D signal and robot node control distribution. Through the analysis of the system time delay and control signal features, we found that the system has good stability, which can satisfy the design need of multi-intelligent robot control. It provides the theoretical reference for the study of ZigBee automation control.

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Image Processing and Recognition System Based on DaVinci Technology for Coal and Gangue

Cited by 6 publications

References 2 publications

Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM

Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM

Foreign Object Detection Method of Conveyor Belt Based on Improved Nanodet

Design of Multi-Intelligent Robot Wireless Communication System Based on ZigBee Automation Control

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