An Image-Based Hierarchical Deep Learning Framework for Coal and Gangue Detection

Abstract

The efficient separation of coal and gangue in the mining process is of great significance for improving coal mining efficiency and reducing environmental pollution. Automatic detection of coal and gangue is the key and foundation for the separation of coal and gangue. In this paper, we proposed a hierarchical framework for coal and gangue detection based on deep learning models. In this framework, the Gaussian pyramid principle is first used to construct multi-level training data, leading to the sets of coal and gangue image features with multiple scales. Then, the coal and gangue regional proposal networks (CG-RPN) are designed to determine the candidate regions of the target objects in the image. Next, convolution neural networks (CNNs) are constructed to recognize coal and gangue objects on the basis of extracted candidate regions. We performed our method on three different datasets. Experimental results showed that the proposed method improves the detection accuracy of coal and gangue objects by 0.8% compared with the previous methods, reaching up to 98.33%. In addition, our proposed method enables the detection of multiple coal and gangue objects in an individual image and solves the problem of queuing requirements in traditional methods.INDEX TERMS Coal and gangue, detection, deep learning, CNNs, multi-level training data.

Automatic Coal and Gangue Segmentation Using U-Net Based Fully Convolutional Networks

“…This section presents the results of the gangue image segmentation obtained by the proposed approach and the comparison experiments with other CNN based approaches in [4,5,10]. Meanwhile, a comprehensive experiment for evaluating the effectiveness of data augmentation was conducted, and the impacts of different input image size were evaluated.…”

“…The sample images used by the previous studies [3,4] contain only a single object. Though several objects were contained in the sample images of [10], the relative positions among them are very sparse. The sample images used in this study were featured the gangue and coal heaped randomly.…”

“…The above architecture is significantly different from the CNN based approach used previously for gangue detection by [4,5,10]. Their architecture contains only the encoder part to extract image features followed by the fully connected layers.…”

“…Pre-treatments of coal and gangue images have been introduced. Li [10] proposed an image-based hierarchical deep learning framework for coal and gangue detection. Gaussian pyramid principle was applied to construct the training dataset, and convolution neural networks were used as a feature extractor to generate the classification and detection box.…”

Coal and Gangue Separating Robot System Based on Computer Vision

“…In order to liberate workers from such dirty, dangerous, and dull (3D) work, a wave of research on industrial robots has been set off. The changes in external information are perceived through vision and fed back to the robot controller, so its application fields will be more extensive [ 9 , 10 ]. Shang proposes a Delta parallel coal gangue online sorting robot based on image recognition and an improved comprehensive calibration method, which avoids the influence of robot installation error on grasping accuracy [ 11 ].…”