Recognizing and distinguishing coal and gangue are essential in engineering, such as in coal-fired power plants. This paper employed a convolutional neural network (CNN) to recognize coal and gangue images and help segregate coal and gangue. A typical workflow for CNN image recognition is presented as well as a strategy for updating the model parameters. Based on a powerful trained image recognition model, VGG16, the idea of transfer learning was introduced to build a custom CNN model to solve the problems of massive trainable parameters and limited computing power linked to the building of a brand-new model from scratch. Two hundred and forty coal and gangue images were collected in a database, including 100 training images and 20 validation images for each material. A recognition accuracy of 82.5% was obtained for the validation images, which demonstrated a decent performance of our model. According to the analysis of parameter updating in the training process, a principal constraint for obtaining a higher recognition accuracy mainly resided in a shortage of training samples. This model was also used to identify photos from a washing plant stockpiles, which verified its capability of dealing with field pictures. CNN combined with the transfer learning method we used can provide fast and robust coal/gangue distinction that does not require harsh data support and equipment support. This method will exhibit brighter prospects in engineering if the target image database (as with the coal and gangue images in this study) can be further enlarged.

https://www.mdpi.com/1996-1073/12/9/1735/pdf

Image Recognition of Coal and Coal Gangue Using a Convolutional Neural Network and Transfer Learning

While coal is the major power source around the globe, gangue is unwanted in power plants. Thus, separating gangue from coal is a crucial part in the preprocessing step of mining. With the development of the computational technologies, it is possible to find one way to enhance the effect of gangue separation. By establishing a coal-gangue separation system based on the difference between coal and gangue in their surface texture and grayscale feature, this paper proposes a method of combining image feature extraction and artificial neural network, to identify gangue. In addition, this method will enable robots, instead of human, to pick the gangue. Ultimately, the automated separation of coal-gangue and increased efficiency of raw coal sorting and quality of coal can be achieved if the method proposed in this paper can be applied in coal industry.

Identification of Coal and Gangue by Feed-forward Neural Network Based on Data Analysis

Recognition and separation of Coal/Gangue are important phases in the coal industries for many aspects. This paper addressed the topic of Coal/Gangue recognition and built a new model called (CGR-CNN) based on Convolutional Neural network (CNN) and using thermal images as standard images for Coal/Gangue recognition. The CGR-CNN model has been developed, augmentation principle has been applied in order to increase the dataset and the best experimental results have been achieved (99.36%) learning accuracy and (95.09%) validation accuracy, in the prediction phase (160) new images of coal and gangue (80 for both) have been tested to measure the efficiency of the work, the prediction result comes with (100%) for coal recognition accuracy and (97.5%) gangue recognition accuracy giving an overall prediction accuracy (98.75%).

/pdf/coal-gangue-recognition-using-convolutional-neural-networks-42gugd6bb8.pdf

Coal/Gangue Recognition Using Convolutional Neural Networks and Thermal Images

Cascade network for detection of coal and gangue in the production context

Ore sorting is a useful tool to remove gangue material from the ore and increase the quality of the ore. The vast developments in the area of artificial intelligence allow fast processing of full-color digital images for the preferred investigations. The associated gangue minerals from limestone and coal mines were identified using three different approaches. All the methods were based on extensions of the co-occurrence matrix method. In the first method, the color features were extracted from RGB color planes and texture features were extracted using a multispectral extension, in which co-occurrence matrices were computed both between and within the color bands. The second method used joint color-texture features where color features were added to gray scale texture features. The last method used gray scale texture features computed on a quantized color image. Results showed that the accuracy for separation of gangue from limestone, a joint color-texture method was 98 % and for separation of gangue from coal, multispectral method with correlation and joint color-texture method were 100 % respectively. Combined multispectral and joint color-texture methods gave good accuracy with 64 gray levels quantization for separation of gangue from limestone and coal.

Novel Methods for Separation of Gangue from Limestone and Coal using Multispectral and Joint Color-Texture Features

We consider the problem of automatically separating coal and coal gangue based on computer vision and design a coal and coal gangue separation system framework based on video. Grayscale histogram, fractal dimension and energy value are extracted as ore features. Then we design a 4-layer Levenberg Marquart BP Neural Network to implement multi-feature fusion. Test results demonstrate that the system has well performance on separation accuracy and its processing speed achieves real-time. It can be used in automatic statistics for open-pit coal output. Moreover, the extended feature vector can be used in coal separation on conveyor belt combined with other automation technology.

Coal and Coal Gangue Separation Based on Computer Vision

Image denoising has been being a hotspot in the discipline of image processing. In this paper, we first analyze the intra-scale distribution and inter-scale distribution characteristics of the coefficients at finer-scale of the noise image, and then propose an edge detection method for preserving important high frequency information through incorporating the coefficients correlation of intra-scale and inter-scale dependencies. Second, we propose a novel image denoising algorithm by combining a zerotree-like structural Bayesian threshold denoising method with a wavelet spatial correlation edge detection method. Experimental results show that the proposed algorithm has steady and efficient result. Not only reserves more edge information, but also do better denoising performance than the traditional Bayes shrinkage method and zerotree-like structural Bayes threshold denoising method’s result.

An Image Denoising Method Based on Wavelet Spatial Correlation Edge Detection and Zerotree-Like Structure

Accurate and efficient human detection has become an important area for research in computer vision. In order to solve problems in the past human detection algorithms such as features with fixed sizes, fixed positions and fixed number, we propose the human detection based on united Hogs algorithm. Through intersection tests and feature integration, the algorithm can dynamically generate the features closer to human body contours. Basically maintaining the detection speed, the detection accuracy is improved by our algorithm.

Fast human detection using a cascade of united hogs

A new integral projection function for eye location—Circular Neighborhood Mean Darkest Intensity Integral Projection Function (CNMDI-IPF) is proposed. Moreover, we propose a fast eye location method via CNMDI-IPF and Sparse Representation. The method works on frontal face images and face rotation is less than 30°. In addition, it can locate eyes from face images with variations in pose, facial expression, and lighting condition, thus it has more robustness and stability in real-life world. The experimental results have shown that our method is superior to other state-of-art methods.

Eye Location Using a New Integral Projection Function and Sparse Representation

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Wenhui Li

Author Tools

Chat about Author

Papers

Coal and Coal Gangue Separation Based on Computer Vision

An Image Denoising Method Based on Wavelet Spatial Correlation Edge Detection and Zerotree-Like Structure

Fast human detection using a cascade of united hogs

Eye Location Using a New Integral Projection Function and Sparse Representation