Research on intelligent detection of coal gangue based on deep learning

Zhang, Yongchao; Wang, Jianshi; Yu, Zhiwei; Zhao, Shuai; Bei, Guangxia

doi:10.1016/j.measurement.2022.111415

Cited by 24 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the mAP and detection speed of Our-v8 were 1.2% and 94.7 FPS higher than their model, respectively. Zhang et al [ 1 ] used YOLOv4 to detect coal and gangue; the mAP and detection speed of Our-v8 were 2% and 114.8 FPS higher than theirs, respectively. Wen et al [ 15 ] proposed a YOLOv5-Swin to detect coal and gangue, with an mAP of 98.6% and a detection speed of 147 FPS.…”

Section: Resultsmentioning

confidence: 99%

“…To effectively improve the detection accuracy of YOLOv8, the loss function is improved from CIOU to EIOU in this study. EIOU loss [ 1 ] is based on the factor of CIOU affecting the aspect ratio and separately calculates the difference between the width and height of the real box and the predicted box. The loss function consists of three parts: IoU loss (L IOU ), center distance loss (L dis ), and aspect ratio loss (L asp ).…”

Section: Methodsmentioning

confidence: 99%

“…And with advancements in artificial intelligence, deep learning algorithms are increasingly employed for coal and gangue identification. Zhang et al [ 1 ] compared the YOLOv4, SSD, and Faster R-CNN algorithms to detect coal gangue and drew the conclusion that the YOLOv4 detection algorithm performed better with an mAP value of 97.52%. Junpeng Zhou et al [ 2 ] presented an improved BASA-LS-FSVM classification algorithm to separate gangue from coal, with separation accuracy reaching 98%.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Coal and Gangue Based on Improved YOLOv8

Zeng,

Zhou,

Wan

et al. 2024

Sensors

View full text Add to dashboard Cite

To address the lightweight and real-time issues of coal sorting detection, an intelligent detection method for coal and gangue, Our-v8, was proposed based on improved YOLOv8. Images of coal and gangue with different densities under two diverse lighting environments were collected. Then the Laplacian image enhancement algorithm was proposed to improve the training data quality, sharpening contours and boosting feature extraction; the CBAM attention mechanism was introduced to prioritize crucial features, enhancing more accurate feature extraction ability; and the EIOU loss function was added to refine box regression, further improving detection accuracy. The experimental results showed that Our-v8 for detecting coal and gangue in a halogen lamp lighting environment achieved excellent performance with a mean average precision (mAP) of 99.5%, was lightweight with FLOPs of 29.7, Param of 12.8, and a size of only 22.1 MB. Additionally, Our-v8 can provide accurate location information for coal and gangue, making it ideal for real-time coal sorting applications.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Coal and Gangue Based on Improved YOLOv8

Zeng,

Zhou,

Wan

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Zhang et al presented a YOLOv4 algorithm based on deep learning for coal gangue detection. The detection algorithm with optimization methods showed higher accuracy, recall rate, and real-time performance compared to the SSD and Faster R-CNN detection algorithms [ 53 ]. Fan et al proposed a coal particle morphology, particle size, liberation feature, and density separation process based on a CNN and an improved U-Net network model [ 54 ].…”

Section: Related Workmentioning

confidence: 99%

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face

Fan,

Mao,

et al. 2024

Sensors

View full text Add to dashboard Cite

With the continuous development of deep learning, the application of object detection based on deep neural networks in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face, it is challenging to meet the computation demands of a large number of hardware resources. Therefore, this paper proposes a lightweight object detection algorithm designed specifically for the coal mining face, referred to as CM-YOLOv8. The algorithm introduces adaptive predefined anchor boxes tailored to the coal mining face dataset to enhance the detection performance of various targets. Simultaneously, a pruning method based on the L1 norm is designed, significantly compressing the model’s computation and parameter volume without compromising accuracy. The proposed algorithm is validated on the coal mining dataset DsLMF+, achieving a compression rate of 40% on the model volume with less than a 1% drop in accuracy. Comparative analysis with other existing algorithms demonstrates its efficiency and practicality in coal mining scenarios. The experiments confirm that CM-YOLOv8 significantly reduces the model’s computational requirements and volume while maintaining high accuracy.

show abstract

“…Recently, deep RL has been applied to various computer vision tasks such as detection [11,12], image enhancement [13][14][15], machine translation [16], and so on. At the same time, RL has also achieved certain research results on low-level image processing tasks.…”

Section: Related Workmentioning

confidence: 99%

Learning filter selection policies for interpretable image denoising in parametrised action space

Xi,

Lyu,

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

The denoising of images is an important research direction in computer vision. We consider the image denoising task as an estimation problem of the filtering policy related to image features, which is different from end‐to‐end image mapping. Commonly used simple filters such as gaussian filtering and bilateral filtering have fixed global denoising policies. However, the denoising policies of different filters can only adapt to limited image features. To solve this problem, we propose a method that applies different filters to different spatial ranges and adjusts the parameters of these filters simultaneously. Since not all filters can be easily transformed into differentiable forms and it is difficult to obtain paired datasets of filter action areas, we use reinforcement learning (RL) methods to estimate the spatial domain action range and adjustable parameters of filters, respectively. Furthermore, for removing higher intensity noise, simple filters can iteratively approximate higher‐order denoising policies and obtain more accurate and stable denoising results with the increase of iteration steps. Experimental results show that our proposed method can not only generate intuitive and interpretable denoising policies but also achieve comparable or better visual effects and computational efficiency than baseline methods.

show abstract

Research on intelligent detection of coal gangue based on deep learning

Cited by 24 publications

References 7 publications

Detection of Coal and Gangue Based on Improved YOLOv8

Detection of Coal and Gangue Based on Improved YOLOv8

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face

Learning filter selection policies for interpretable image denoising in parametrised action space

Contact Info

Product

Resources

About