{"title":"MobileNet-Tiny:基于深度神经网络的Rasberry Pi实时目标检测","authors":"Nithesh Singh Sanjay, A. Ahmadinia","doi":"10.1109/ICMLA.2019.00118","DOIUrl":null,"url":null,"abstract":"In this paper, we present a new neural network architecture, MobileNet-Tiny that can be used to harness the power of GPU based real-time object detection in raspberry-pi and also in devices with the absence of a GPU and limited graphic processing capabilities such as mobile phones, laptops, etc. MobileNet-Tiny trained on COCO dataset running on a non-Gpu laptop dell XPS 13, achieves an accuracy of 19.0 mAP and a speed of 19.4 FPS which is 3 times as fast as MobileNetV2, and when running on a raspberry pi, it achieves a speed of 4.5 FPS which is up to 7 times faster than MobileNetV2. MobileNet-Tiny was modeled to offer a compact, quick, and well-balanced object detection solution to a variety of GPU restricted devices.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"MobileNet-Tiny: A Deep Neural Network-Based Real-Time Object Detection for Rasberry Pi\",\"authors\":\"Nithesh Singh Sanjay, A. Ahmadinia\",\"doi\":\"10.1109/ICMLA.2019.00118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a new neural network architecture, MobileNet-Tiny that can be used to harness the power of GPU based real-time object detection in raspberry-pi and also in devices with the absence of a GPU and limited graphic processing capabilities such as mobile phones, laptops, etc. MobileNet-Tiny trained on COCO dataset running on a non-Gpu laptop dell XPS 13, achieves an accuracy of 19.0 mAP and a speed of 19.4 FPS which is 3 times as fast as MobileNetV2, and when running on a raspberry pi, it achieves a speed of 4.5 FPS which is up to 7 times faster than MobileNetV2. MobileNet-Tiny was modeled to offer a compact, quick, and well-balanced object detection solution to a variety of GPU restricted devices.\",\"PeriodicalId\":436714,\"journal\":{\"name\":\"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLA.2019.00118\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2019.00118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MobileNet-Tiny: A Deep Neural Network-Based Real-Time Object Detection for Rasberry Pi
In this paper, we present a new neural network architecture, MobileNet-Tiny that can be used to harness the power of GPU based real-time object detection in raspberry-pi and also in devices with the absence of a GPU and limited graphic processing capabilities such as mobile phones, laptops, etc. MobileNet-Tiny trained on COCO dataset running on a non-Gpu laptop dell XPS 13, achieves an accuracy of 19.0 mAP and a speed of 19.4 FPS which is 3 times as fast as MobileNetV2, and when running on a raspberry pi, it achieves a speed of 4.5 FPS which is up to 7 times faster than MobileNetV2. MobileNet-Tiny was modeled to offer a compact, quick, and well-balanced object detection solution to a variety of GPU restricted devices.