Target detection in mine based on fusion of color and edge information

Xian, Xiaodong; Jiang, Qiqi; Li, Xudong

doi:10.1109/icamechs.2017.8316498

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…In the study of traditional image features, Xian et al [ 16 ] improved the target detection accuracy by improving the target detection method of the Gaussian mixture model. Hou et al [ 17 ] proposed a method for the recognition of coal gangue based on a neural network by analyzing the differences in surface textures and grayscale characteristics between coal and gangue.…”

Section: Literature Reviewmentioning

confidence: 99%

A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS

Wang

Guo

Zhao

et al. 2022

Sensors

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With the aim of solving the problem of coal congestion caused by big coal blocks in underground mine scraper conveyors, in this paper we proposed the use of a YOLO-BS (YOLO-Big Size) algorithm to detect the abnormal phenomenon of coal blocks on scraper conveyors. Given the scale of the big coal block targets, the YOLO-BS algorithm replaces the last layer of the YOLOv4 algorithm feature extraction backbone network with the transform module. The YOLO-BS algorithm also deletes the redundant branch which detects small targets in the PAnet module, which reduces the overall number of parameters in the YOLO-BS algorithm. As the up-sampling and down-sampling operations in the PAnet module of the YOLO algorithm can easily cause feature loss, YOLO-BS improves the problem of feature loss and enhances the convergence performance of the model by adding the SimAM space and channel attention mechanism. In addition, to solve the problem of sample imbalance in big coal block data, in this paper, it was shown that the YOLO-BS algorithm selects focal loss as the loss function. In view of the problem that the same lump coal in different locations on the scraper conveyor led to different congestion rates, we conducted research and proposed a formula to calculate the congestion rate. Finally, we collected 12,000 image datasets of coal blocks on the underground scraper conveyor in Daliuta Coal Mine, China, and verified the performance of the method proposed in this paper. The results show that the processing speed of the proposed method can reach 80 fps, and the correct alarm rate can reach 93%. This method meets the real-time and accuracy requirements for the detection of abnormal phenomena in scraper conveyors.

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Section: Literature Reviewmentioning

confidence: 99%

A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS

Wang

Guo

Zhao

et al. 2022

Sensors

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Online shearer-onboard personnel detection method for the intelligent fully mechanized mining face

Wei

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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As unmanned coal mining technology gradually replaces the traditional mechanized coal mining technology, shearer operation mode is changed from local control to remote control in intelligent fully mechanized mining face. In remote control mode, it is difficult to protect the personnel who straying into the shearer operator space without observation and reminder from the shearer operator. Hence, it is necessary to establish an intelligent personnel detection method to protect the safety of coal miners in intelligent unmanned mining face. The environment of low and non-uniform illumination in fully mechanized coal mining face has seriously restricted the application of personnel detection technology based on visible light imaging. Therefore, a personnel detection method based on infrared thermal imaging is proposed in this paper to solve the disadvantages of using visible light imaging in downhole applications. On this basis, a spatiotemporal guided filter is proposed to harmonize the relationship between edge-preserving and noise-removing. Then, an improved Lucas-Kanade method based on the adaptive-size window is utilized to achieve a more robust personnel detection. Moreover, the personnel detection in the shearer operating space is realized based on epipolar geometry and morphology processing. Finally, the laboratory experiment and industrial test are carried out to evaluate the proposed method, and the results indicate the feasibility and superiority of the proposed methods which show considerable application prospects.

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Object Detection Algorithm in Underground Mine Based on Sparse Representation

Lu¹,

Huang²

2018

Int J Adv Tech

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This paper puts forward an improved K-SVD object detection algorithm for the problem of multiple noise sources in underground mine video. Firstly, the background modeling is applied in the video; then, the improved non-local mean filtering algorithm is used to enhance the image quality; finally, the improved image is processed by the sparse representation algorithm to further detect the moving object. In order to verify the effectiveness of the proposed algorithm, the algorithm and other algorithms are applied to video object detection in two different scenarios. The experimental results show that, in the underground mine video, the proposed algorithm can increase the accuracy by more than 8% compared with the traditional K-SVD algorithm, and the proportion of error points decreases by about 25%. Better detection of the moving object is achieved by the proposed algorithm.

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Target detection in mine based on fusion of color and edge information

Cited by 3 publications

References 20 publications

A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS

A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS

Online shearer-onboard personnel detection method for the intelligent fully mechanized mining face

Object Detection Algorithm in Underground Mine Based on Sparse Representation

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