计算机科学与探索2024，Vol.18Issue(1)：127-137,11.DOI:10.3778/j.issn.1673-9418.2209065
结合密度图回归与检测的密集计数研究
Counting Method Based on Density Graph Regression and Object Detection
摘要
Abstract
In response to the low recall rate of detection-based methods and the problem of missing target location information in density-based methods,which are the two mainstream dense-counting methods,a detection and counting method based on density map regression is proposed by combining the two tasks,achieving the counting and positioning of target objects in dense scenes.Complementing the advantages of two methods not only improves recall rate but also calibrates all targets.To extract richer feature information to deal with complex data scenarios,a feature pyramid optimization module is proposed,which vertically fuses low-level high-resolution features with top-level abstract semantic features and horizontally fuses same-size features to enrich the semantic expression of target objects.To address the issue of low pixel proportions occupied by target objects in dense counting scenarios,an attention mechanism for small targets is proposed to improve the network's detection sensitivity,which can enhance the attention of the network to target objects by constructing a mask on the input image.Experimental results demonstrate that the proposed method significantly improves recall rate and accurately locates targets while maintaining accuracy,effectively providing counting and positioning information of input image,which has a wide range of application prospects in various fields such as industry and ecology.关键词
密集计数/目标检测/深度学习/密度图回归/特征金字塔Key words
intensive count/target detection/deep learning/density map regression/feature pyramid分类
信息技术与安全科学引用本文复制引用
高洁,赵心馨,于健,徐天一,潘丽,杨珺,喻梅,李雪威..结合密度图回归与检测的密集计数研究[J].计算机科学与探索,2024,18(1):127-137,11.基金项目
天津市企业科技特派员项目(20YDTPJC01570).This work was supported by the Technical Export Project of Tianjin(20YDTPJC01570). （20YDTPJC01570）
