基于K-Means算法的SSD-Mobilenet模型优化研究

Research on SSD-Mobilenet model optimization based on K-Means algorithm

SSD-Mobilenet目标检测模型是将SSD和Mobilenet进行结合衍生出的一种轻量化模型,同时具备了两模型各自的优势,即多尺度检测和模型轻量化。在原模型中特征提取层使用了人为设置的先验框,这样的设置存在一定的主观性,并不适用于对特定场景下单一类别目标的识别与定位。为解决这一问题,本文提出了使用K-Means算法对目标真实框的宽高比进行聚类分析,提升模型在特定场景下对单一类别目标的检测能力,规避了人为设置的主观先验性。使用Pascal VOC 2007数据集对该模型进行训练和评估,实验结果显示,模型的mAP值比Fast RCNN提高了4.5%,比Faster RCNN提高了1.5%,比SSD-300提高了3.4%,比YOLOv2提高了2.4%。

The SSD-Mobilenet target detection model is a lightweight model derived from the combination of SSD and Mobilenet.It also has the advantages of the two models,namely multi-scale detection and lightweight model.In the original model,the feature extraction layer uses artificially set a priori boxes.Such settings are subjective and unsuitable for the recognition and positioning of single-category targets in specific scenarios.In order to solve this problem,this paper proposes to use the K-Means algorithm to perform cluster analysis on the aspect ratio of the real frame of the target,which improves the model′s ability to detect a single category of targets in a specific scenario,and avoids the subjective apriority of artificial settings.This paper uses the Pascal VOC 2007 data set to train and evaluate the model.The experimental results show that the mAP value of the model is 4.5%higher than Fast-RCNN,1.5%higher than Faster-RCNN,3.4%higher than SSD-300,YOLOv2 increased by 2.4%.