{{위키데이터 속성 추적}}
[[파일:Detected-with-YOLO--Schreibtisch-mit-Objekten.jpg|섬네일|80개의 공통 객체를 탐지할 수 있는 COCO 데이터셋에서 훈련된 YOLOv3 모델을 사용함으로써 OpenCV의 딥 뉴럴 네트워크로 감지된 물체.]]
'''객체 탐지'''(客體探知, {{lang|en|object detection}})는 [[컴퓨터 비전]]과 [[디지털 화상 처리|이미지 처리]]와 관련된 컴퓨터 기술로서, 디지털 이미지와 비디오로 특정한 계열의 시맨틱 객체 인스턴스(예: 인간, 건물, 자동차)를 감지하는 일을 다룬다. 잘 연구된 객체 탐지 분야로는 [[얼굴 검출]], [[보행자 검출]]이 포함된다. 객체 탐지는 [[영상 복구]], [[폐쇄회로 텔레비전|비디오 감시]]를 포함한 수많은 컴퓨터 비전 분야에 응용되고 있다.

== 방식 ==
객체 탐지를 위한 방식은 일반적으로 [[기계 학습]] 기반 접근 또는 [[딥 러닝]] 기반 접근으로 분류된다. 기계 학습 접근의 경우 우선 아래의 방식들 가운데 하나를 사용하여 정의한 다음 [[서포트 벡터 머신]](SVM) 등의 기법을 사용하여 분류하는 일이 필요하다. 한편, 딥 러닝 기법은 기능을 구체적으로 정의하지 않고서도 단대단 객체 탐지를 할 수 있으며 [[합성곱 신경망]](CNN)에 기반을 두는 것이 보통이다.

* 기계 학습 접근:
** [[하르 특징]](Haar features) 기반 [[비올라–존스 객체탐지 프레임워크]]
** [[척도 불변 특징 변환]](Scale-invariant feature transform, SIFT)
** [[경사지향 히스토그램]](Histogram of oriented gradients, HOG) 기능<ref>{{저널 인용 |last1=Dalal |first1=Navneet |title=Histograms of oriented gradients for human detection |journal=Computer Vision and Pattern Recognition |date=2005 |volume=1 |url=https://hal.inria.fr/file/index/docid/548512/filename/hog_cvpr2005.pdf |확인날짜=2019-11-15 |보존url=https://web.archive.org/web/20190617101239/https://hal.inria.fr/file/index/docid/548512/filename/hog_cvpr2005.pdf |보존날짜=2019-06-17 |url-status=dead }}</ref>
* 딥 러닝 접근:
** Region Proposals (R-CNN,<ref>{{저널 인용|last1=Ross|first1=Girshick|date=2014|title=Rich feature hierarchies for accurate object detection and semantic segmentation|url=https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf|journal=Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition|publisher=IEEE|volume=|pages=|doi=10.1109/CVPR.2014.81|via=|확인날짜=2019-11-15|보존url=https://web.archive.org/web/20191202124904/https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf|보존날짜=2019-12-02|url-status=dead}}</ref> Fast R-CNN,<ref>{{저널 인용|last1=Girschick|first1=Ross|date=2015|title=Fast R-CNN|url=https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf|journal=Proceedings of the IEEE International Conference on Computer Vision|volume=|pages=1440–1448|arxiv=1504.08083|via=|확인날짜=2019-11-15|보존url=https://web.archive.org/web/20191031101927/https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf|보존날짜=2019-10-31|url-status=dead}}</ref> Faster R-CNN<ref>{{저널 인용|last1=Shaoqing|first1=Ren|date=2015|title=Faster R-CNN|url=https://arxiv.org/pdf/1506.01497.pdf|journal=Advances in Neural Information Processing Systems|volume=|pages=|arxiv=1506.01497|via=}}</ref>, cascade R-CNN<ref name="Pang Chen Shi Feng 2019">{{ArXiv 인용| last1=Pang | first1=Jiangmiao | last2=Chen | first2=Kai | last3=Shi | first3=Jianping | last4=Feng | first4=Huajun | last5=Ouyang | first5=Wanli | last6=Lin | first6=Dahua | title=Libra R-CNN: Towards Balanced Learning for Object Detection | date=2019-04-04 | class=cs.CV | eprint=1904.02701v1 }}</ref>)
** Single Shot MultiBox Detector (SSD)<ref>{{서적 인용|last1=Liu |first1=Wei |title=SSD: Single shot multibox detector |journal=European Conference on Computer Vision |volume=9905 |date=October 2016 |pages=21–37 |arxiv=1512.02325 |doi=10.1007/978-3-319-46448-0_2 |series=Lecture Notes in Computer Science |isbn=978-3-319-46447-3 }}</ref>
** You Only Look Once (YOLO)<ref>{{저널 인용|last1=Redmon |first1=Joseph |title=You only look once: Unified, real-time object detection |journal=Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition |date=2016|arxiv=1506.02640 }}</ref><ref>{{ArXiv 인용|last1=Redmon |first1=Joseph |title=YOLO9000: better, faster, stronger |date=2017 |eprint=1612.08242|class=cs.CV }}</ref><ref>{{ArXiv 인용|last1=Redmon |first1=Joseph |title=Yolov3: An incremental improvement |eprint=1804.02767 |date=2018 |class=cs.CV }}</ref><ref name="yolov4">{{ArXiv 인용|last1=Bochkovskiy |first1=Alexey |title=Yolov4: Optimal Speed and Accuracy of Object Detection |eprint=2004.10934 |date=2020 |class=cs.CV }}</ref>
** Single-Shot Refinement Neural Network for Object Detection (RefineDet)<ref>{{서적 인용|last1=Zhang |first1=Shifeng |title=Single-Shot Refinement Neural Network for Object Detection |journal=Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition|date=2018 |pages=4203–4212 |arxiv=1711.06897}}</ref>
** Retina-Net<ref>{{저널 인용|last1=Lin |first1=Tsung-Yi |title=Focal Loss for Dense Object Detection |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |volume=42 |issue=2 |pages=318–327 |arxiv=1708.02002|pmid=30040631 |doi=10.1109/TPAMI.2018.2858826 |bibcode=2017arXiv170802002L |year=2020 |s2cid=47252984 }}</ref><ref name="Pang Chen Shi Feng 2019">{{ArXiv 인용| last1=Pang | first1=Jiangmiao | last2=Chen | first2=Kai | last3=Shi | first3=Jianping | last4=Feng | first4=Huajun | last5=Ouyang | first5=Wanli | last6=Lin | first6=Dahua | title=Libra R-CNN: Towards Balanced Learning for Object Detection | date=2019-04-04 | class=cs.CV | eprint=1904.02701v1 }}</ref>
** Deformable convolutional networks<ref>{{ArXiv 인용|last1=Zhu |first1=Xizhou |title=Deformable ConvNets v2: More Deformable, Better Results |date=2018 |eprint=1811.11168|class=cs.CV }}</ref><ref>{{ArXiv 인용|last1=Dai |first1=Jifeng |title=Deformable Convolutional Networks |date=2017 |eprint=1703.06211|class=cs.CV }}</ref>

== 같이 보기 ==
* [[객체 인식 개요]]
== 각주 ==
{{각주}}
*{{웹 인용 |url=http://vision.eecs.ucf.edu/projects/3D_Model_based_Object_Detection/ObjectDetection.html |title=Object Class Detection |publisher=Vision.eecs.ucf.edu |date= |accessdate=2013-10-09 |보존url=https://web.archive.org/web/20130714115654/http://vision.eecs.ucf.edu/projects/3D_Model_based_Object_Detection/ObjectDetection.html |보존날짜=2013-07-14 |url-status=dead }}
*{{웹 인용 |url=http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en |title=ETHZ - Computer Vision Lab: Publications |publisher=Vision.ee.ethz.ch |date= |accessdate=2013-10-09 |보존url=https://web.archive.org/web/20130603170209/http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en |보존날짜=2013-06-03 |url-status=dead }}

== 외부 링크 ==
* [https://web.archive.org/web/20150418092531/http://www.mmp.rwth-aachen.de/publications/pdf/mikolajczyk-multiclass-cvpr06.pdf Multiple object class detection]
* [https://qilin-zhang.github.io/_pages/pdfs/Segment-Tube_Spatio-Temporal_Action_Localization_in_Untrimmed_Videos_with_Per-Frame_Segmentation.pdf Spatio-temporal action localization]
* [https://web.archive.org/web/20180907032435/https://qilin-zhang.github.io/_pages/pdfs/Joint_Video_Object_Discovery_and_Segmentation_by_Coupled_Dynamic_Markov_Networks.pdf Video object detection and co-segmentation]

[[분류:감시]]