Multi-scale coal and gangue detection in dense state based on improved Mask RCNN

Wang, Xi; Wang, Shuang; Guo, Yongcun; Jia, Xiaofen; Hu, Kun; Cheng, Gang

doi:10.1016/j.measurement.2023.113467

Cited by 7 publications

(2 citation statements)

References 44 publications

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“…Intelligent devices have seldom been utilized for efficient and intelligent identification of lump coal. Currently, the detection of large lumps of coal relies mainly on manual identification, which is time-consuming and inefficient [6]. Furthermore, low illumination and high dust levels in the underground working environment typically cause uncontrollable disturbances for manual identification, posing additional challenges for lump coal recognition.…”

Section: Introductionmentioning

confidence: 99%

SNW YOLOv8: improving the YOLOv8 network for real-time monitoring of lump coal

Wu,

Chen,

et al. 2024

Meas. Sci. Technol.

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Due to the large size of the coal and the high mining output, lump coal is one of the hidden risks of mining conveyor damage. Typically, lump coal can cause jamming and even damage to the conveyor belt during the coal mining and transportation process. This study proposes a novel real-time detection method for lump coal on a conveyor belt. The Space-to-Depth Conv (SPD-Conv) module is introduced into the feature extraction network to extract the features of the mine's low-resolution lump coal. To enhance the feature extraction capability of the model, the Normalization-based Attention Module (NAM) is combined to adjust weight sparsity. After loss function optimization using the Wise-IoU v3 (WIoU v3) module, the SPD-Conv-NAM-WIoU v3 YOLOv8 (SNW YOLO v8) model is proposed. The experimental results show that the SNW YOLOv8 model outperforms the widely used model (YOLOv8) in terms of precision and recall by 15.82% and 11.71%, respectively. Significantly, the real-time detection speed of the SNW YOLOv8 model is increased to 192.93 f/s. Compared to normal models, the SNW YOLO v8 model overcomes the disadvantages of normal models, such as being overweight, and the parameters of SNW YOLO v8 are reduced to only 6.04 million with a small model volume of 12.3 MB. Meanwhile, the floating point of SNW YOLOv8 is significantly reduced. Consequently, it demonstrates excellent lump coal detection performance, which may open up a new window for coal mining optimization.

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Section: Introductionmentioning

confidence: 99%

SNW YOLOv8: improving the YOLOv8 network for real-time monitoring of lump coal

Wu,

Chen,

et al. 2024

Meas. Sci. Technol.

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“…He et al 20 utilized optoelectronics to obtain independent image targets and proposed a concave surface point detection and segmentation algorithm, which translates the concave point detection into a positional relationship between a pixel and a linear equation, and finally generates a segmentation line to segment the image based on the concave points. Wang et al 21 developed a multibranch parallel feature extraction bottleneck to establish a lightweight backbone network and then designed a novel neck structure to aggregate channel and spatial information into the contextual information of the backbone network to enhance the location and boundary information of the coal and gangue, which effectively provides the category and location information of the coal and gangue in the dense state. He et al 22 proposed pit detection and segmentation algorithms to solve the target sticking and overlapping problems, design open‐loop and closed‐loop crossover algorithms, use conjugate line to detect pits to determine the position and distance of pixel points relative to the conjugate line, and then set the distance constraints to get the segmentation line corresponding to the pits using the minimum distance search method to realize coal and gangue segmentation.…”

Section: Introductionmentioning

confidence: 99%

Research on coal and gangue segmentation based on MFCCM‐Mask R‐CNN

Cao,

Li,

Fang

et al. 2024

Energy Science & Engineering

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Intelligent sorting of coal and gangue is of great significance to the intelligent construction of coal mines as well as green development. In this study, we propose a coal and gangue segmentation method with an improved classical segmentation network Mask R‐CNN, denoted as Multichannel Forward‐Linked Confusion Convolution Module (MFCCM)‐Mask R‐CNN. First, we design a MFCCM to construct the feature extraction network by stacking, second, we design a multiscale high‐resolution feature pyramid network structure to realize multipath fusion of feature information to enhance the position and contour information of the target, and finally, we propose a multiscale Mask head to enhance the diversity of information, and capture the more representative and unique features. Training and testing models using self‐built RGB coal and gangue data sets, the accuracy of the improved algorithm reaches 97.38%, which is an improvement of 1.66% compared to the original model. Compared with other segmentation models Unet, Deeplab V3+, Yoloact, Yolov7, and the model after replacing the backbone network, the MFCCM‐Mask R‐CNN has higher precision and recall, and can more accurately realize the efficient segmentation of coal and gangue.

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