Research On Coal Dust Particle Size Detection Method Based On Convolutional Neural Network

Coal dust control is the foundation of coal mine safety,and particle size distribution(PSD)of coal is the supporting information to test the dust removal effect.The PSD of coal dust can be accurately obtained by manual screening,instrument detection and other methods,but the equipment is expensive and labor costs are high,making it difficult for large-scale application in coal mine scenarios.Therefore,on the basis of analyzing the existing particle segmentation and statistical theory,this paper studies the detection algorithm of coal dust PSD based on convolutional neural network.Firstly,aiming at the low segmentation accuracy caused by the large number of particles and irregular contours in coal dust images,an M-SegNet network model is proposed.In the codec structure of the model,coal dust features are extracted through depth wise convolution and pointwise convolution,which reduces the computational burden of the model in terms of the number of operations and parameters.An attention mechanism is introduced to obtain spatial and channel weight matrices,and global semantic information is integrated to optimize feature extraction capabilities.In the decoding stage,the encoding position is recorded to establish a position index,and the feature map size is restored by nonlinear up-sampling.Experiments are carried out on coal dust images of different sizes,and the optimal parameters of the model are fitted to effectively segment the particles of coal dust images.Secondly,aiming at the statistical error caused by adhered particles,a segmentation algorithm based on concave points search is proposed.On the basis of judging the adhered particle area,the Gauss-Canny operator is used to extract the contour of the segmented binary image and filter out noise.The feature points with obvious grayscale transformation are extracted by sliding Harris matrix to form the initial concave point sequence.Combined with the angle and area constraints of concave points,the pseudo-concave points formed by the concave particles are eliminated.Finally,the minimum Euclidean distance of the concave points is calculated to construct a dividing line to separate the boundary of the adhered particles.The experimental results show that the algorithm in this paper can accurately separate the adhered particles without damaging the geometric characteristics of the particles.Finally,for the coal dust segmentation algorithm based on convolutional neural network proposed in this paper,particle size distribution experiments are carried out.Firstly,the characteristics of the test samples are evaluated.The results show that the average recognition accuracy of the number of particles is 95.35%,and the error rate is 2.64%.Secondly,the quantitative particle size distribution experiments are carried out by defining five particle sizes.Compared with the manual sieving method,the mean values of the frequency distributions errors of Feret diameter,Martin diameter,ellipse diameter,perimeter diameter and area diameter are 1.43%,2.49%,1.63%,1.80%and 1.77%,respectively.Combined with the cumulative distribution curve of the above particle size,it shows that the elliptical diameter and the Feret diameter can accurately evaluate the PSD of coal dust.In addition,a multiple linear regression equation is constructed to predict the thickness through the geometric characteristics of coal dust,and the mass particle size distribution of coal dust with three particle sizes of 0～75μm,75～200μm and>200μm are evaluated.The mean values of the mass distributions errors of Feret diameter,Martin diameter,ellipse diameter,perimeter diameter and area diameter are 4.75%、2.45%、2.05%、4.56%and 5.29%respectively,indicating that the mass conversion model proposed in this paper is feasible.In this study,we integrate coal mine safety and image processing disciplines,and design an overall detection method for coal dust particle size distribution.The research results can enrich the relevant theories of coal dust images,improve the segmentation accuracy of coal dust images,and then accurately evaluate the PSD information of coal dust,which has theoretical and practical significance.