A lightweight object detection algorithm for coal and gangue with laser speckle imaging

IF 3.5 2区 工程技术 Q2 OPTICS Optics and Lasers in Engineering Pub Date : 2024-10-12 DOI:10.1016/j.optlaseng.2024.108630
Hequn Li, Ling Ling, Yufei Zheng, Hanxi Yang, Yun Liu, Mingxing Jiao
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Abstract

Laser speckle imaging, known for its ability to capture object surface features with simple setup and reduced sensitivity to ambient light, is of significant research interest for coal and gangue recognition. However, the complex surface structures of minerals in practical settings pose challenges in improving recognition accuracy over large sample sizes and extended periods using manual feature design. To address this issue, we propose a coal and gangue recognition method that integrates laser speckle imaging with deep learning. Based on objective speckle imaging theory, we designed a coal and gangue laser speckle image acquisition system and curated a dataset encompassing diverse lighting conditions for speckle imaging. We developed a lightweight YOLOv5s model to extract rich surface information from mineral laser speckle images, achieving high-precision coal and gangue detection while reducing computational demands. Experimental results demonstrate significant improvements in model size, training effectiveness, feature extraction, and recognition accuracy by lightening the YOLOv5s model. Furthermore, our method exhibits improved accuracy and stability in coal and gangue recognition under varying lighting conditions compared to manual feature design approach. Additionally, our model strikes a balance between complexity and accuracy, offering practical advantages over existing models for industrial applications. These findings provide valuable technical support for the future realization of intelligent coal and gangue recognition.
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利用激光斑点成像技术检测煤炭和矸石的轻量级物体检测算法
激光斑点成像技术能够捕捉物体表面特征,设置简单,对环境光的敏感度较低,在煤炭和煤矸石识别方面具有重要的研究意义。然而,在实际环境中,矿物的表面结构复杂,这给使用人工特征设计提高大样本量和长时间的识别准确率带来了挑战。针对这一问题,我们提出了一种将激光斑点成像与深度学习相结合的煤炭和煤矸石识别方法。基于客观斑点成像理论,我们设计了一套煤炭和煤矸石激光斑点图像采集系统,并策划了一个包含不同光照条件的斑点成像数据集。我们开发了轻量级的 YOLOv5s 模型,从矿物激光斑点图像中提取丰富的表面信息,实现了高精度的煤炭和煤矸石检测,同时降低了计算需求。实验结果表明,通过轻量化 YOLOv5s 模型,在模型大小、训练效果、特征提取和识别准确性方面都有显著改善。此外,与人工特征设计方法相比,我们的方法在不同光照条件下识别煤炭和煤矸石的准确性和稳定性都有所提高。此外,我们的模型在复杂性和准确性之间取得了平衡,与工业应用中的现有模型相比具有实际优势。这些发现为未来实现智能煤炭和煤矸石识别提供了宝贵的技术支持。
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来源期刊
Optics and Lasers in Engineering
Optics and Lasers in Engineering 工程技术-光学
CiteScore
8.90
自引率
8.70%
发文量
384
审稿时长
42 days
期刊介绍: Optics and Lasers in Engineering aims at providing an international forum for the interchange of information on the development of optical techniques and laser technology in engineering. Emphasis is placed on contributions targeted at the practical use of methods and devices, the development and enhancement of solutions and new theoretical concepts for experimental methods. Optics and Lasers in Engineering reflects the main areas in which optical methods are being used and developed for an engineering environment. Manuscripts should offer clear evidence of novelty and significance. Papers focusing on parameter optimization or computational issues are not suitable. Similarly, papers focussed on an application rather than the optical method fall outside the journal''s scope. The scope of the journal is defined to include the following: -Optical Metrology- Optical Methods for 3D visualization and virtual engineering- Optical Techniques for Microsystems- Imaging, Microscopy and Adaptive Optics- Computational Imaging- Laser methods in manufacturing- Integrated optical and photonic sensors- Optics and Photonics in Life Science- Hyperspectral and spectroscopic methods- Infrared and Terahertz techniques
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