Investigations of object detection in images/videos using various deep learning techniques and embedded platforms-A comprehensive review
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Murthy C.B. | - |
dc.contributor.author | Hashmi M.F. | - |
dc.contributor.author | Bokde N.D. | - |
dc.contributor.author | Geem Z.W. | - |
dc.date.available | 2020-06-15T01:35:24Z | - |
dc.date.created | 2020-06-03 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/53433 | - |
dc.description.abstract | In recent years there has been remarkable progress in one computer vision application area: object detection. One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene. Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area. This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques. Several topics have been included, such as Viola-Jones (VJ), histogram of oriented gradient (HOG), one-shot and two-shot detectors, benchmark datasets, evaluation metrics, speed-up techniques, and current state-of-art object detectors. Detailed discussions on some important applications in object detection areas, including pedestrian detection, crowd detection, and real-time object detection on Gpu-based embedded systems have been presented. At last, we conclude by identifying promising future directions. © 2020 by the authors. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI AG | - |
dc.relation.isPartOf | Applied Sciences (Switzerland) | - |
dc.title | Investigations of object detection in images/videos using various deep learning techniques and embedded platforms-A comprehensive review | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000535541900293 | - |
dc.identifier.doi | 10.3390/app10093280 | - |
dc.identifier.bibliographicCitation | Applied Sciences (Switzerland), v.10, no.9 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85085064580 | - |
dc.citation.title | Applied Sciences (Switzerland) | - |
dc.citation.volume | 10 | - |
dc.citation.number | 9 | - |
dc.contributor.affiliatedAuthor | Geem Z.W. | - |
dc.type.docType | Review | - |
dc.subject.keywordAuthor | Computer vision (CV) | - |
dc.subject.keywordAuthor | Convolutional neural network (CNN) | - |
dc.subject.keywordAuthor | Deep learning techniques | - |
dc.subject.keywordAuthor | Graphics processing units (GPUs) | - |
dc.subject.keywordAuthor | Object detection | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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