基于改进YOLOv5s的肠镜息肉多分类实时检测方法
A real-time multi-class detection method for colonoscopy polyps based on improved YOLOv5s
薛林雁 1李轩昂 2齐晁仪 2曹杰 2张颖 2艾尚璞 2杨昆1
作者信息
- 1. 河北大学质量技术监督学院,河北保定 071002;计量仪器与系统国家地方联合工程研究中心,河北保定 071002;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
- 2. 河北大学质量技术监督学院,河北保定 071002
- 折叠
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.
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 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.
关键词
息肉/腺瘤/检测/YOLOv5s/实时性Key words
polyps/adenomatous polyps/object detection/YOLOv5s/real-time引用本文复制引用
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)
河北大学研究生创新项目(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)
大学生创新创业训练计划创新训练项目(DC2024381)
出版年
2024
NETL
NSTL
万方数据