@article{
 title = {Object Detection using YOLO: A Survey},
 type = {article},
 year = {2022},
 keywords = {Computer Vision,Object Detection,Regression,State-of-the-art,YOLO},
 pages = {747-752},
 publisher = {IEEE},
 id = {b7d883ec-f010-35a4-b46e-8810989cedcc},
 created = {2024-03-16T12:43:16.030Z},
 file_attached = {true},
 profile_id = {f1f70cad-e32d-3de2-a3c0-be1736cb88be},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2024-03-16T12:43:19.325Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 folder_uuids = {df28411a-ed7f-4991-8358-d39685eb4bf0},
 private_publication = {false},
 abstract = {In recent years, object detection is becoming very popular field in computer vision developments. Object detection has many applications viz. vehicle detection, pedestrian detection, blood cell counting etc. Various studies have been conducted in order to improve object detecting accuracy and speed. The latest technique is You Only Look Once object detection. It is state-of-the-art detection technique and considered as a regression problem. YOLO has the ability to predict various objects present in an image in a single run. This paper presents a survey of various detections based on YOLO which aims to improve the accuracy of existing system. This paper presents various modifications done on basic YOLO method and shows their analysis.},
 bibtype = {article},
 author = {Tripathi, Abhinandan and Gupta, Manish Kumar and Srivastava, Chaynika and Dixit, Pallavi and Pandey, Shrawan Kumar},
 doi = {10.1109/IC3I56241.2022.10073281},
 journal = {Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022},
 number = {December 2022}
}

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