中圖分類號(hào)： TP391
文獻(xiàn)標(biāo)識(shí)碼： A
DOI： 10.19358/j.issn.2096-5133.2021.01.007
引用格式： 劉津龍，賈郭軍。 基于K-Means算法的SSD-Mobilenet模型優(yōu)化研究[J].信息技術(shù)與網(wǎng)絡(luò)安全，2021，40（1）：37-44.

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

Abstract： 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%.

Key words : object detection；K-Means；SSD-Mobilenet；anchor box；cluster