Adriana Carrillo Rios, Douglas Henke dos Reis, Rodrigo Mattos da Silva, Marco Antonio de Souza Leite Cuadros, D. Gamarra
{"title":"基于YOLOv3和SSD MobileNet v2的室内机器人图像目标识别算法比较","authors":"Adriana Carrillo Rios, Douglas Henke dos Reis, Rodrigo Mattos da Silva, Marco Antonio de Souza Leite Cuadros, D. Gamarra","doi":"10.1109/INDUSCON51756.2021.9529585","DOIUrl":null,"url":null,"abstract":"The YOLO and SSD algorithms are tools widely used for detecting objects in images or videos. This is due to the speed of detection and good performance in the identification of objects. This article presents a comparison of the YOLOv3 and SSD MobileNet v2 algorithms for identifying objects in images through simulations, the dataset used is an indoor robotics dataset. In order to reach the objective, several training sessions were carried out to analyze the behavior of each model when detecting objects in images. After analyzing the results, a better performance of the YOLOv3 model was observed, although this model takes more time to complete the training for the same number of steps compared to the SSD MobileNet v2 model. It is worth mentioning that this work presents for the first time a comparison between the SSD MobileNet v2 and YOLOv3 algorithms.","PeriodicalId":344476,"journal":{"name":"2021 14th IEEE International Conference on Industry Applications (INDUSCON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Comparison of the YOLOv3 and SSD MobileNet v2 Algorithms for Identifying Objects in Images from an Indoor Robotics Dataset\",\"authors\":\"Adriana Carrillo Rios, Douglas Henke dos Reis, Rodrigo Mattos da Silva, Marco Antonio de Souza Leite Cuadros, D. Gamarra\",\"doi\":\"10.1109/INDUSCON51756.2021.9529585\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The YOLO and SSD algorithms are tools widely used for detecting objects in images or videos. This is due to the speed of detection and good performance in the identification of objects. This article presents a comparison of the YOLOv3 and SSD MobileNet v2 algorithms for identifying objects in images through simulations, the dataset used is an indoor robotics dataset. In order to reach the objective, several training sessions were carried out to analyze the behavior of each model when detecting objects in images. After analyzing the results, a better performance of the YOLOv3 model was observed, although this model takes more time to complete the training for the same number of steps compared to the SSD MobileNet v2 model. It is worth mentioning that this work presents for the first time a comparison between the SSD MobileNet v2 and YOLOv3 algorithms.\",\"PeriodicalId\":344476,\"journal\":{\"name\":\"2021 14th IEEE International Conference on Industry Applications (INDUSCON)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 14th IEEE International Conference on Industry Applications (INDUSCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDUSCON51756.2021.9529585\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 14th IEEE International Conference on Industry Applications (INDUSCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDUSCON51756.2021.9529585","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of the YOLOv3 and SSD MobileNet v2 Algorithms for Identifying Objects in Images from an Indoor Robotics Dataset
The YOLO and SSD algorithms are tools widely used for detecting objects in images or videos. This is due to the speed of detection and good performance in the identification of objects. This article presents a comparison of the YOLOv3 and SSD MobileNet v2 algorithms for identifying objects in images through simulations, the dataset used is an indoor robotics dataset. In order to reach the objective, several training sessions were carried out to analyze the behavior of each model when detecting objects in images. After analyzing the results, a better performance of the YOLOv3 model was observed, although this model takes more time to complete the training for the same number of steps compared to the SSD MobileNet v2 model. It is worth mentioning that this work presents for the first time a comparison between the SSD MobileNet v2 and YOLOv3 algorithms.
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