基于GN分裂的小目标检测区域推荐搜索算法

An Algorithm of Small Object Detection Region Proposal Search Based on GN Splitting

区域推荐搜索是机器视觉研究热点之一,针对传统目标检测使用穷举式搜索效率低下的问题,通过优化搜索的准确率可提高检测效率。引入复杂网络中用于社区发现的Girvan-Newman(GN)分裂算法,结合小目标区域特征,提出一种基于图像网络结构的小目标检测区域推荐搜索算法。该算法根据区域间多样性颜色直方图相似性构建图像与图的映射关系,通过图中连通子图的生成获取小目标可能区域。能在生成较少候选区的情况下满足较高的召回率,进一步优化小目标检测的时间消耗。

The region proposal search is one of the most active research topics of machine vision.The low efficiency of object detection using the traditional exhaustive search can be improved by optimizing the accuracy of search algorithm.The Girvan-Newman（GN）splitting for community discovery in complex networks is introduced as well as the features of small object regions.A novel method to generate small object regions is proposed by using the network structure of image.The algorithm constructs the relationship between the images and the graphs based on the similarity of color histograms between regions.It can obtain possible regions through the generation of connected subgraphs.This algorithm can meet the higher recall rate in the case of generating fewer candidate regions and further optimize the time consumption of small object detection.