A Method of Ore Image Segmentation Based on Deep Learning

Li, Yuan; Duan, Yingying

doi:10.1007/978-3-319-95957-3_53

Cited by 14 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their main objective was to optimize blasting parameters and enhance the efficiency of open-pit mining operations. Yuan et al [13] introduced a deep learning-based method for rock image segmentation, utilizing annotated datasets to train an overall nested boundary detection model for segmenting rock blocks. Ko et al [14] applied image processing techniques to acquire the surface of rock blocks and built a neural network model to analyze the distribution of block sizes.…”

Section: Computation Methods Based On the Image Modalitymentioning

confidence: 99%

Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images

Peng,

Cui,

Zhong

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

The accurate calculation of ore rock fragmentation is important for achieving the autonomous mining operation of mine excavators. However, a single mode cannot accurately calculate the ore rock fragmentation due to the low resolution of the point cloud mode and the lack of spatial position information of the image mode. To solve this problem, we propose an ore rock fragmentation calculation method (ORFCM) based on the multi-modal fusion of point clouds and images. The ORFCM makes full use of the advantages of multi-modal data, including the fine-grained object segmentation of images and spatial location information of point clouds. To solve the problem of image under-segmentation, we propose a multiscale adaptive edge-detection method based on an innovative standard deviation map to enhance the weak edges. Furthermore, an improved marked watershed segmentation algorithm is proposed to solve the problem of low segmentation accuracy caused by excessive noise of the gradient map and weak edges submerged. Experiments demonstrate that ORFCM can accurately calculate ore rock fragmentation in the local excavation area without relying on external markers for pixel calibration. The average error of the equivalent diameter of ore rock blocks is 0.66 cm, the average error of the elliptical long diameter is 1.42 cm, and the average error of the elliptical short diameter is 1.06 cm, which can effectively meet practical engineering needs.

show abstract

Section: Computation Methods Based On the Image Modalitymentioning

confidence: 99%

Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images

Peng,

Cui,

Zhong

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…As for semantic segmentation, the best segmentation results of different scenes are from U-Net [ 12 ], PSP-Net [ 13 ], DeepLabv3+ [ 14 ], and their variants. In ore image segmentation, CNN-based techniques have also shown great advantages over traditional image processing methods [ 15 ]. Liu et al [ 16 ] adopted U-Net to preliminarily segment ore images and then use Res-Unet to optimize the segmentation masks.…”

Section: Introductionmentioning

confidence: 99%

An Improved Boundary-Aware U-Net for Ore Image Semantic Segmentation

Wang

Chengyong

et al. 2021

Sensors

View full text Add to dashboard Cite

Particle size is the most important index to reflect the crushing quality of ores, and the accuracy of particle size statistics directly affects the subsequent operation of mines. Accurate ore image segmentation is an important prerequisite to ensure the reliability of particle size statistics. However, given the diversity of the size and shape of ores, the influence of dust and light, the complex texture and shadows on the ore surface, and especially the adhesion between ores, it is difficult to segment ore images accurately, and under-segmentation can be a serious problem. The construction of a large, labeled dataset for complex and unclear conveyor belt ore images is also difficult. In response to these challenges, we propose a novel, multi-task learning network based on U-Net for ore image segmentation. To solve the problem of limited available training datasets and to improve the feature extraction ability of the model, an improved encoder based on Resnet18 is proposed. Different from the original U-Net, our model decoder includes a boundary subnetwork for boundary detection and a mask subnetwork for mask segmentation, and information of the two subnetworks is fused in a boundary mask fusion block (BMFB). The experimental results showed that the pixel accuracy, Intersection over Union (IOU) for the ore mask (IOU_M), IOU for the ore boundary (IOU_B), and error of the average statistical ore particle size (ASE) rate of our proposed model on the testing dataset were 92.07%, 86.95%, 52.32%, and 20.38%, respectively. Compared to the benchmark U-Net, the improvements were 0.65%, 1.01%, 5.78%, and 12.11% (down), respectively.

show abstract

“…Mukherjee et al [7] developed a neural network to enhance the uneven light images and to learn rock shape features. Yuan et al [8] used a deep learning method to solve the mutual adhesion and shadow problems in the rock images. Besides the generalized neural network structures, the convolutional neural network (CNN)-based algorithms present significant advantages in object detection and semantic segmentation [9].…”

Section: Introductionmentioning

confidence: 99%

Image Process of Rock Size Distribution Using DexiNed-Based Neural Network

Asbjörnsson

Lindqvist

2021

Minerals

View full text Add to dashboard Cite

In an aggregate crushing plant, the crusher performances will be affected by the variation from the incoming feed size distribution. Collecting accurate measurements of the size distribution on the conveyors can help both operators and control systems to make the right decisions in order to reduce overall power consumption and avoid undesirable operating conditions. In this work, a particle size distribution estimation method based on a DexiNed edge detection network, followed by the application of contour optimization, is proposed. The proposed framework was carried out in the four main steps. The first step, after image preprocessing, was to utilize a modified DexiNed convolutional neural network to predict the edge map of the rock image. Next, morphological transformation and watershed transformation from the OpenCV library were applied. Then, in the last step, the mass distribution was estimated from the pixel contour area. The accuracy and efficiency of the DexiNed method were demonstrated by comparing it with the ground-truth segmentation. The PSD estimation was validated with the laboratory screened rock samples.

show abstract

A Method of Ore Image Segmentation Based on Deep Learning

Cited by 14 publications

References 11 publications

Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images

Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images

An Improved Boundary-Aware U-Net for Ore Image Semantic Segmentation

Image Process of Rock Size Distribution Using DexiNed-Based Neural Network

Contact Info

Product

Resources

About