Efficient and Lightweight Framework for Real-Time Ore Image Segmentation Based on Deep Learning
Open accessAbstract
Image segmentation approaches have been utilized to determine the particle size distribution of crushed ores in the past decades. It is not possible to deploy large and high-powered computing equipment due to the complex working environment, so existing algorithms are difficult to apply in practical engineering. This article presents a novel efficient and lightweight framework for ore image segmentation to discern full and independent ores. First, a lightweight backbone is introduced for feature extraction while reducing computational complexity. Then, we propose a compact pyramid network to process the data obtained from the backbone to reduce unnecessary semantic information and computation. Finally, an optimized detection head is proposed to obtain the feature to maintain accuracy. Extensive experimental results demonstrate the effectiveness of our method, which achieves 40 frames per second on our new ore image dataset with a very small model size. Meanwhile, our method maintains a high level of accuracy—67.68% in AP50box and 46.73% in AP50mask—compared with state-of-the-art approaches.
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The contradiction between accuracy and speed is one of the main problems faced by defect recognition algorithms.
“The contradiction between accuracy and speed is also one of the main problems faced by the defect recognition algorithm [10]”
From Steel Surface Defect Recognition: A Survey (2022) · cites this paper as [10]
The Introduction describes a speed–precision trade-off in instance segmentation, and Section 4.3.2 says improving inference speed can cost accuracy. The paper studies ore image segmentation, however, not defect recognition algorithms specifically; that application is not supported by the cited source. (LLM, conf=0.86)
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- Authors
- Sun, G., Huang, D., Cheng, L., Jia, J., Xiong, C., & Zhang, Y.
- Affiliations
- Hubei University of Technology (CN)
- Corresponding author
- Zhang, Yang
- Venue
- Minerals
- Publisher
- MDPI AG
- Published
- 2022-04-23
- Volume
- 12
- Issue
- 5
- Pages
- 526
- ISSN
- 2075-163X (ISSN-L)
- OpenAlex
- W4224308289
- Cited by
- 24
- References
- 49
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 4 · 2023: 7 · 2022: 4
- FWCI
- 1.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Minerals (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (51775177)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Mechanical Engineering › Mineral Processing and Grinding
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- particle size distribution, lightweight neural networks, real-time segmentation
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