海南热带海洋学院学报2025，Vol.32Issue(2)：106-113,8.DOI:10.13307/j.issn.2096-3122.2025.02.11
基于改进YOLOv4的遮挡目标识别算法
Occlusion Target Recognition Algorithm Based on Improved YOLOv4
摘要
Abstract
In occlusion target recognition,the target may be occluded by other objects,resulting in the loss or deformation of some effective features of the target.The reduction of effective features of the target makes the single YOLOv4(You Only Look Once version 4)unable to accurately recognize the initial value of the anchor box,making the model target difficult to recognize.For this reason,K-means++algorithm was introduced to improve the single YOLOv4 algorithm,and a occlusion target recognition algorithm based on the improved YOLOv4 was proposed.The image was divided into low frequency and high frequency parts by non-subsampled Contourlet transform.The linear enhancement function and improved adaptive threshold were used to enhance the image,and the reconstructed image was generated by non-subsampled Contourlet inverse transformation,which performed the fuzzy contrast enhancement.YOLOv4 was selected as the basic model of target recognition,and the depth-separable convolution was used to re-place part of the convolution in the model,and the feature pyramid was replaced with a recursive feature pyramid to improve the fea-ture learning ability of small targets and occluded targets.The K-Means++algorithm was introduced to adaptively obtain the anchor frame,optimize the initial value of the anchor frame,and construct the loss function by using the complete intersection ratio and cross entropy,and train the improved YOLOv4 and input the enhanced image into it to realize the occlusion target recognition.The experimental results showed that the proposed method can effectively recognize image targets and the P-R curve of recognition results is more ideal.关键词
YOLOv4/遮挡目标识别/非下采样Contourlet变换/深度可分离卷积/递归特征金字塔Key words
YOLOv4/occlusion target recognition/non-subsampled Contourlet transform/depthwise separable convolution/recursive feature pyramid分类
计算机与自动化引用本文复制引用
裴云霞,黄忠..基于改进YOLOv4的遮挡目标识别算法[J].海南热带海洋学院学报,2025,32(2):106-113,8.基金项目
安徽省高等学校科学研究项目(2022AH052783) （2022AH052783）
