ArticleRSC advances2020
Ore image segmentation method using U-Net and Res_Unet convolutional networks.
Article in RSC advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Blasting ore size detection based on efficient dehazing network and multi-dimensional feature fusion.Scientific reports · 2026Article
- VTC-Net: A Semantic Segmentation Network for Ore Particles Integrating Transformer and Convolutional Block Attention Module (CBAM).Sensors (Basel, Switzerland) · 2026Article
- HU-FlowNet: A Method for Trajectory Detection in Dense Granular Flows Integrating the Hungarian Algorithm, U‑Net, and UnLiteFlowNet.ACS omega · 2025Article
- Large ore detection in blasting piles using LODM.Scientific reports · 2025Article
- Research on anti-clogging of ore conveyor belt with static image based on improved Fast-SCNN and U-Net.Scientific reports · 2023Article
- A deep learning framework to scale linear facial measurements to actual size using horizontal visible iris diameter: a study on an Iranian population.Scientific reports · 2023Article
- Study on segmentation of blasting fragment images from open-pit mine based on U-CARFnet.PloS one · 2023Article
- Deep learning architectures for diagnosing the severity of apple frog-eye leaf spot disease in complex backgrounds.Frontiers in plant science · 2023Article
- A method of blasted rock image segmentation based on improved watershed algorithm.Scientific reports · 2022Article
- Grape Leaf Disease Classification Combined with U-Net++ Network and Threshold Segmentation.Computational intelligence and neuroscience · 2022Article
- Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning.Computational intelligence and neuroscience · 2022Article
- Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging.Frontiers in oncology · 2022Article
- Deep Learning for Prediction of Progression and Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features.Frontiers in oncology · 2022Article
- Rock Particle Motion Information Detection Based on Video Instance Segmentation.Sensors (Basel, Switzerland) · 2021Article
- Deep Learning Based Instance Segmentation of Titanium Dioxide Particles in the Form of Agglomerates in Scanning Electron Microscopy.Nanomaterials (Basel, Switzerland) · 2021Article
- An Improved Boundary-Aware U-Net for Ore Image Semantic Segmentation.Sensors (Basel, Switzerland) · 2021Article
- A modern deep learning framework in robot vision for automated bean leaves diseases detection.International journal of intelligent robotics and applications · 2021Article
- Automated Segmentation Method for Low Field 3D Stomach MRI Using Transferred Learning Image Enhancement Network.BioMed research international · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Image segmentation has been increasingly used to identify the particle size distribution of crushed ore; however, the adhesion of ore particles and dark areas in the images of blast heaps and conveyor belts usually results in lower segmentation accuracy. To overcome this issue, an image segmentation method UR based on deep learning U-Net and Res_Unet networks is proposed in this study. Gray-scale, median filter and adaptive histogram equalization techniques are used to preprocess the original ore images captured from an open pit mine to reduce noise and extract the target region. U-Net and Res_Unet are utilized to generate ore contour detection and optimization models, and the ore image segmentation result is illustrated by OpenCV. The efficiency and accuracy of the newly proposed UR method is demonstrated and validated by comparing with the existing image segmentation methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.