{"title":"基于RFB模块和注意机制的目标检测算法","authors":"志青 王","doi":"10.12677/sea.2023.125067","DOIUrl":null,"url":null,"abstract":"Aiming at the problem of insufficient correlation of feature map extracted by deep convolutional network in object detection algorithm, an improved SSD object detection algorithm based on Re-ceptive Field Block and Coordinate Attention is proposed. The deep feature extraction network ResNet50 is used as the backbone network, and a coordinate attention module is added to the convolutional layer structure to capture the information of direction and location awareness. In","PeriodicalId":69507,"journal":{"name":"软件工程与应用","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Object Detection Algorithm Based on RFB Module and Attention Mechanism\",\"authors\":\"志青 王\",\"doi\":\"10.12677/sea.2023.125067\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the problem of insufficient correlation of feature map extracted by deep convolutional network in object detection algorithm, an improved SSD object detection algorithm based on Re-ceptive Field Block and Coordinate Attention is proposed. The deep feature extraction network ResNet50 is used as the backbone network, and a coordinate attention module is added to the convolutional layer structure to capture the information of direction and location awareness. In\",\"PeriodicalId\":69507,\"journal\":{\"name\":\"软件工程与应用\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"软件工程与应用\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12677/sea.2023.125067\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"软件工程与应用","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12677/sea.2023.125067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Object Detection Algorithm Based on RFB Module and Attention Mechanism
Aiming at the problem of insufficient correlation of feature map extracted by deep convolutional network in object detection algorithm, an improved SSD object detection algorithm based on Re-ceptive Field Block and Coordinate Attention is proposed. The deep feature extraction network ResNet50 is used as the backbone network, and a coordinate attention module is added to the convolutional layer structure to capture the information of direction and location awareness. In
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