A Diverse Environment Coal Gangue Image Segmentation Model Combining Improved U-Net and Semi-supervised Automatic Annotation

Liu, Xiuhua; Zhu, Wenbo; Zhu, Zhengjun; Luo, Lufeng; Zhang, Yunzhi; Lu, Qinghua

doi:10.1007/978-981-99-8021-5_13

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Fu et al 23 proposed a fast clustering segmentation algorithm for water pixels based on gradient enhancement, which enhances the edge gradient features by multiscale details, then reconstructs the gradient watershed transform based on multiscale morphology, and finally performs statistics and clustering on the obtained hyperpixel map to get the final effect map. Liu et al 24 introduced the InceptionV1 module to replace some of these convolutional blocks based on U‐net and integrated the CPAM attention module to solve the light change problem, which improved the segmentation accuracy based on the original model. Li et al 25 proposed a segmentation method based on deep learning of convolutional neural network in mask region, which can recognize fine coal dust, and combined with focus loss and dice coefficient new mask loss function to overcome the wrong segmentation caused by edge effect, and achieved effective results.…”

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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Section: Introductionmentioning

confidence: 99%