A real-time multi-class detection method for colonoscopy polyps based on improved YOLOv5s

doi:10.3969/j.issn.1000-1565.2024.04.011

Abstract

Abstract: To facilitate rapid identification and detection of colorectal polyps during colonoscopy procedures, a real-time multi-class detection model for colonoscopic polyps based on modified YOLOv5s is proposed. This model utilizes ConvNeXt as thebackbone network and incorporates the SimAM attention mechanism to improve detection performance. Additionally, a slim-neck module based on GSConv is employed in the neck network to reduce network parameters. For model training and testing, a colorectal polyp dataset containing 1 676 images annotated by professional doctors was constructed. The proposed model achieves a mean Average Precision(mAP@0.5)of 83.0% on the test set, which is an improvement- DOI:10.3969/j.issn.1000-1565.2024.04.011基于改进YOLOv5s的肠镜息肉多分类实时检测方法薛林雁^1,2,3,李轩昂¹,齐晁仪¹,曹杰¹,张颖¹,艾尚璞¹,杨昆^1,2,3(1.河北大学质量技术监督学院,河北保定 071002;2.计量仪器与系统国家地方联合工程研究中心,河北保定 071002;3.河北省新能源汽车动力系统轻量化技术创新中心,河北保定 071002)摘要:为了在肠镜检查过程中对结直肠息肉进行快速鉴别检测,提出一种基于改进YOLOv5s的肠镜息肉多分类实时检测模型.该模型采用ConvNeXt作为主干网络, 融入SimAM注意力机制提升检测性能,同时在颈部网络中使用基于GSConv的slim-neck模块减少网络参数.为了对模型进行训练和测试,构建了包含1 676张息肉图像并由专业医生标注的结直肠息肉数据集.提出的模型在测试集上的平均精度均值(mAP@0.5)为83.0%,相较于改进前提升8.4%,检测速度达到120帧/s. 此外,模型在边缘侧部署检测速度超过25帧/s.结果表明,改进的YOLOv5s满足临床结肠镜检查对实时性与准确性的要求.关键词:息肉;腺瘤;检测;YOLOv5s;实时性中图分类号:TP391.7 文献标志码:A 文章编号:1000-1565(2024)04-0424-09A real-time multi-class detection method for colonoscopy polyps based on improved YOLOv5sXUE Linyan^1,2,3, LI Xuanang¹, QI Chaoyi¹, CAO Jie¹, ZHANG Ying¹, AI Shangpu¹, YANG Kun^1,2,3(1. College of Quality and Technical Supervision, Hebei University, Baoding 071002, China; 2. National & Local Joint Engineering Research Center of Metrology Instrument and System, Baoding 071002, China; 3. New Energy Vehicle Power System Lightweight Technology Innovation Center of Hebei Province, Baoding 071002, China)Abstract: To facilitate rapid identification and detection of colorectal polyps during colonoscopy procedures, a real-time multi-class detection model for colonoscopic polyps based on modified YOLOv5s is proposed. This model utilizes ConvNeXt as thebackbone network and incorporates the SimAM attention mechanism to improve detection performance. Additionally, a slim-neck module based on GSConv is employed in the neck network to reduce network parameters. For model training and testing, a colorectal polyp dataset containing 1 676 images annotated by professional doctors was constructed. The proposed model achieves a mean Average Precision(mAP@0.5)of 83.0% on the test set, which is an improvement- 收稿日期:2024-03-16;修回日期:2024-05-06 基金项目:河北省自然科学基金资助项目(F2023201069);保定市创新能力提升专项项目(2394G027);河北大学研究生创新项目(HBU2024BS021;HBU2024SS011);河北大学科研创新团队项目(IT2023B07);大学生创新创业训练计划创新训练项目(DC2024376;DC2024381) 第一作者:薛林雁(1981—),女,河北大学副教授,主要从事生物医学图像处理方向研究.E-mail: lyxue@hbu.edu.cn 通信作者:杨昆(1976—),男,河北大学教授,博士生导师,主要从事生物医学图像处理方向研究.E-mail: yangkun@hbu.edu.cn第4期薛林雁等:基于改进YOLOv5s的肠镜息肉多分类实时检测方法河北大学学报(自然科学版) 第44卷of 8.4% compared to the model before modification, with a detection speed of 120 frames per second. Moreover,the model exhibits a detection speed exceeding 25 frames per second when deployed on edge devices. The results demonstrate that the improved YOLOv5s meets the clinical requirementsfor real-time and accurate colonoscopy examinations.

Key words: polyps, adenomatous polyps, object detection, YOLOv5s, real-time

CLC Number:

TP391.7

XUE Linyan, LI Xuanang, QI Chaoyi, CAO Jie, ZHANG Ying, AI Shangpu, YANG Kun. A real-time multi-class detection method for colonoscopy polyps based on improved YOLOv5s[J]. Journal of Hebei University(Natural Science Edition), 2024, 44(4): 424-432.

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