YOLORIS: YOLO for Real-time Instance Segmentation

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 108 === In recent years, image segmentation has become an important issue, and been used in many fields like self-driving cars, computer vision, video tracking, medical use and so on. Therefore, there are many researchers devote themselves into this challenge. Image...

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Main Authors: Lu, Yan-Ting, 盧彥廷
Other Authors: Tsai, Wen-Jiin
Format: Others
Language:en_US
Published: 2019
Online Access:http://ndltd.ncl.edu.tw/handle/yyu3py
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spelling ndltd-TW-108NCTU53940012019-11-26T05:16:53Z http://ndltd.ncl.edu.tw/handle/yyu3py YOLORIS: YOLO for Real-time Instance Segmentation 基於YOLO物體偵測進行即時的實例分割 Lu, Yan-Ting 盧彥廷 碩士 國立交通大學 資訊科學與工程研究所 108 In recent years, image segmentation has become an important issue, and been used in many fields like self-driving cars, computer vision, video tracking, medical use and so on. Therefore, there are many researchers devote themselves into this challenge. Image segmentation is to classify image in pixel-wise, making machine to learn and realize locations and classes of instances in images. However, predicting instances precisely is a critical issue. There are many papers work on it, but most of them focus on accuracy instead of speed, needing lots of hardware to support. In this paper, we present a real-time and good performance method that only need one GPU to support which is based on YOLOv3. Tsai, Wen-Jiin Chen, Hua-Tsung 蔡文錦 陳華總 2019 學位論文 ; thesis 27 en_US
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language en_US
format Others
sources NDLTD
description 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 108 === In recent years, image segmentation has become an important issue, and been used in many fields like self-driving cars, computer vision, video tracking, medical use and so on. Therefore, there are many researchers devote themselves into this challenge. Image segmentation is to classify image in pixel-wise, making machine to learn and realize locations and classes of instances in images. However, predicting instances precisely is a critical issue. There are many papers work on it, but most of them focus on accuracy instead of speed, needing lots of hardware to support. In this paper, we present a real-time and good performance method that only need one GPU to support which is based on YOLOv3.
author2 Tsai, Wen-Jiin
author_facet Tsai, Wen-Jiin
Lu, Yan-Ting
盧彥廷
author Lu, Yan-Ting
盧彥廷
spellingShingle Lu, Yan-Ting
盧彥廷
YOLORIS: YOLO for Real-time Instance Segmentation
author_sort Lu, Yan-Ting
title YOLORIS: YOLO for Real-time Instance Segmentation
title_short YOLORIS: YOLO for Real-time Instance Segmentation
title_full YOLORIS: YOLO for Real-time Instance Segmentation
title_fullStr YOLORIS: YOLO for Real-time Instance Segmentation
title_full_unstemmed YOLORIS: YOLO for Real-time Instance Segmentation
title_sort yoloris: yolo for real-time instance segmentation
publishDate 2019
url http://ndltd.ncl.edu.tw/handle/yyu3py
work_keys_str_mv AT luyanting yolorisyoloforrealtimeinstancesegmentation
AT lúyàntíng yolorisyoloforrealtimeinstancesegmentation
AT luyanting jīyúyolowùtǐzhēncèjìnxíngjíshídeshílìfēngē
AT lúyàntíng jīyúyolowùtǐzhēncèjìnxíngjíshídeshílìfēngē
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