改进YOLOv4框架的胃息肉检测

在计算机视觉的内窥胃部息肉检测中, 高效提取小型息肉图像特征是设计深度学习的计算机视觉模型一个难点. 针对该问题, 提出了一种YOLOv4改进的YOLOv4-polyp检测模型. 首先在YOLOv4的基础上, 引入CBAM卷积注意力模块增强模型在复杂环境的特征提取能力; 其次设计出轻量级CSPDarknet-49网络模型, 在降低模型复杂度的同时提高检测精度和检测速度; 最后根据胃息肉数据集的特点, 采用K-means++聚类算法对胃息肉数据集进行聚类分析, 得到优化后的锚框. 实验对比结果表明, YOLOv4-polyp对于经典YOLOv4模型在保持检测速率不变的同时, 在两个数据集中平均检测精度分别提升了5.21%和2.05%, 表现出良好的检测性能.

Improved YOLOv4 Framework for Gastric Polyp Detection

In endoscopic gastric polyp detection based on computer vision, efficiently extracting the features of images of small polyps is a difficulty in the design of a deep learning-based computer vision model. To solve this problem, this study proposes a detection model based on an improved you only look once version 4 (YOLOv4), namely YOLOv4-polyp. Specifically, on the basis of YOLOv4, this study adds a convolutional block attention module (CBAM) to enhance the feature extraction capability of the model in complex environments. Then, a lightweight CSPDarknet-49 network model is designed to both reduce the complexity of the model and improve its detection accuracy and detection speed. Finally, considering the characteristics of the gastric polyp datasets, the K-means++ clustering algorithm is used for the cluster analysis of the gastric polyp datasets and the attainment of the optimized anchor box. The experimental comparison results show that compared with the classical YOLOv4 model, the proposed YOLOv4-polyp achieves favorable detection performance on the two datasets as it improves the average detection accuracy by 5.21% and 2.05%, respectively, without compromising the detection speed.