Small object detection: Difference between revisions

Revision as of 23:21, 15 November 2023

Revision as of 00:24, 14 December 2023

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== Uses ==

[[File:Track_Results.webm|thumb|An example of object tracking]]

Small object detection has applications in various fields such as Video [[surveillance]] (Traffic video Surveillance,<ref>{{Cite book |last1=Saran K B |last2=Sreelekha G |title=2015 International Conference on Control Communication & Computing India (ICCC) |chapter=Traffic video surveillance: Vehicle detection and classification |chapter-url=https://ieeexplore.ieee.org/document/7432948 |year=2015 |___location=Trivandrum, Kerala, India |publisher=IEEE |pages=516–521 |doi=10.1109/ICCC.2015.7432948 |isbn=978-1-4673-7349-4|s2cid=14779393 }}</ref><ref>{{Cite journal |last=Nemade |first=Bhushan |date=2016-01-01 |title=Automatic Traffic Surveillance Using Video Tracking |url=https://www.sciencedirect.com/science/article/pii/S1877050916001836 |journal=Procedia Computer Science |series=Proceedings of International Conference on Communication, Computing and Virtualization (ICCCV) 2016 |language=en |volume=79 |pages=402–409 |doi=10.1016/j.procs.2016.03.052 |issn=1877-0509|doi-access=free }}</ref> [[Content-based image retrieval|Small object retrieval]],<ref>{{Cite book |last1=Guo |first1=Haiyun |last2=Wang |first2=Jinqiao |last3=Xu |first3=Min |last4=Zha |first4=Zheng-Jun |last5=Lu |first5=Hanqing |title=Proceedings of the 23rd ACM international conference on Multimedia |chapter=Learning Multi-view Deep Features for Small Object Retrieval in Surveillance Scenarios |date=2015-10-13 |chapter-url=https://doi.org/10.1145/2733373.2806349 |series=MM '15 |___location=New York, NY, USA |publisher=Association for Computing Machinery |pages=859–862 |doi=10.1145/2733373.2806349 |isbn=978-1-4503-3459-4|s2cid=9041849 }}</ref><ref>{{Cite journal |last1=Galiyawala |first1=Hiren |last2=Raval |first2=Mehul S. |last3=Patel |first3=Meet |date=2022-05-20 |title=Person retrieval in surveillance videos using attribute recognition |url=https://doi.org/10.1007/s12652-022-03891-0 |journal=Journal of Ambient Intelligence and Humanized Computing |language=en |doi=10.1007/s12652-022-03891-0 |s2cid=248951090 |issn=1868-5145}}</ref> [[Anomaly detection]],<ref>{{Cite journal |last1=Ingle |first1=Palash Yuvraj |last2=Kim |first2=Young-Gab |date=2022-05-19 |title=Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities |journal=Sensors |language=en |volume=22 |issue=10 |pages=3862 |doi=10.3390/s22103862 |issn=1424-8220 |pmc=9143895 |pmid=35632270|bibcode=2022Senso..22.3862I |doi-access=free }}</ref> [[Maritime surveillance]], [[Aerial survey|Drone surveying]], [[Traffic flow|Traffic flow analysis]],<ref>{{Cite journal |last1=Tsuboi |first1=Tsutomu |last2=Yoshikawa |first2=Noriaki |date=2020-03-01 |title=Traffic flow analysis in Ahmedabad (India) |url=https://www.sciencedirect.com/science/article/pii/S2213624X18301974 |journal=Case Studies on Transport Policy |language=en |volume=8 |issue=1 |pages=215–228 |doi=10.1016/j.cstp.2019.06.001 |s2cid=195543435 |issn=2213-624X|doi-access=free }}</ref> and [[Video tracking|Object tracking]].

== Problems with small objects ==

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==== Choosing a data set that has small objects ====

The [[machine learning]] model's output depends on "How well it is trained."<ref name=":0">{{Cite journal |last1=Gong |first1=Zhiqiang |last2=Zhong |first2=Ping |last3=Hu |first3=Weidong |date=2019 |title=Diversity in Machine Learning |url=https://ieeexplore.ieee.org/document/8717641 |journal=IEEE Access |volume=7 |pages=64323–64350 |doi=10.1109/ACCESS.2019.2917620 |s2cid=206491718 |issn=2169-3536|doi-access=free }}</ref> So, the data set must include small objects to detect such objects. Also, modern-day detectors, such as YOLO, rely on anchors.<ref>{{Cite web |last=Christiansen |first=Anders |date=2022-06-10 |title=Anchor Boxes — The key to quality object detection |url=https://towardsdatascience.com/anchor-boxes-the-key-to-quality-object-detection-ddf9d612d4f9 |access-date=2022-09-14 |website=Medium |language=en}}</ref> Latest versions of YOLO (starting from YOLOv5<ref>{{cite journal |last1=Jocher |first1=Glenn |title=ultralytics/yolov5: v6.2 - YOLOv5 Classification Models, Apple M1, Reproducibility, ClearML and Deci.ai integrations |date=2022-08-17 |url=https://zenodo.org/record/7002879 |doi=10.5281/zenodo.3908559 |access-date=2022-09-14 |last2=Chaurasia |first2=Ayush |last3=Stoken |first3=Alex |last4=Borovec |first4=Jirka |last5=NanoCode012 |last6=Kwon |first6=Yonghye |last7=TaoXie |last8=Michael |first8=Kalen |last9=Fang |first9=Jiacong}}</ref>) uses an auto-anchor algorithm to find good anchors based on the nature of object sizes in the data set. Therefore, it is mandatory to have smaller objects in the data set.

==== Generating more data via augmentation, if required ====

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* Crowd counting<ref>{{Cite book |last1=Rajendran |first1=Logesh |last2=Shyam Shankaran |first2=R |title=2021 IEEE International Conference on Big Data and Smart Computing (BigComp) |chapter=Bigdata Enabled Realtime Crowd Surveillance Using Artificial Intelligence and Deep Learning |chapter-url=https://ieeexplore.ieee.org/document/9373133 |year=2021 |___location=Jeju Island, Korea (South) |publisher=IEEE |pages=129–132 |doi=10.1109/BigComp51126.2021.00032 |isbn=978-1-7281-8924-6|s2cid=232236614 }}</ref><ref>{{Cite book |last1=Sivachandiran |first1=S. |last2=Mohan |first2=K. Jagan |last3=Nazer |first3=G. Mohammed |title=2022 6th International Conference on Computing Methodologies and Communication (ICCMC) |chapter=Deep Transfer Learning Enabled High-Density Crowd Detection and Classification using Aerial Images |date=2022-03-29 |chapter-url=https://ieeexplore.ieee.org/document/9753982 |___location=Erode, India |publisher=IEEE |pages=1313–1317 |doi=10.1109/ICCMC53470.2022.9753982 |isbn=978-1-6654-1028-1|s2cid=248131806 }}</ref><ref>{{Cite book |last1=Santhini |first1=C. |last2=Gomathi |first2=V. |title=2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) |chapter=Crowd Scene Analysis Using Deep Learning Network |chapter-url=https://ieeexplore.ieee.org/document/8550851 |year=2018 |pages=1–5 |doi=10.1109/ICCTCT.2018.8550851|isbn=978-1-5386-3702-9 |s2cid=54438440 }}</ref><ref>{{Cite book |last1=Sharath |first1=S.V. |last2=Biradar |first2=Vidyadevi |last3=Prajwal |first3=M.S. |last4=Ashwini |first4=B. |title=2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) |chapter=Crowd Counting in High Dense Images using Deep Convolutional Neural Network |date=2021-11-19 |chapter-url=https://ieeexplore.ieee.org/document/9663716 |___location=Nitte, India |publisher=IEEE |pages=30–34 |doi=10.1109/DISCOVER52564.2021.9663716 |isbn=978-1-6654-1244-5|s2cid=245707782 }}</ref>

* Vehicle re-identification<ref>{{Cite journal |last1=Wang |first1=Hongbo |last2=Hou |first2=Jiaying |last3=Chen |first3=Na |date=2019 |title=A Survey of Vehicle Re-Identification Based on Deep Learning |url=https://ieeexplore.ieee.org/document/8915694 |journal=IEEE Access |volume=7 |pages=172443–172469 |doi=10.1109/ACCESS.2019.2956172 |s2cid=209319743 |issn=2169-3536|doi-access=free }}</ref>

* Animal detection<ref>{{Cite book |last1=Santhanam |first1=Sanjay |last2=B |first2=Sudhir Sidhaarthan |last3=Panigrahi |first3=Sai Sudha |last4=Kashyap |first4=Suryakant Kumar |last5=Duriseti |first5=Bhargav Krishna |title=2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) |chapter=Animal Detection for Road safety using Deep Learning |date=2021-11-26 |chapter-url=https://ieeexplore.ieee.org/document/9697287 |___location=Nagpur, India |publisher=IEEE |pages=1–5 |doi=10.1109/ICCICA52458.2021.9697287 |isbn=978-1-6654-2040-2|s2cid=246663727 }}</ref><ref>{{Cite book |last1=Li |first1=Nopparut |last2=Kusakunniran |first2=Worapan |last3=Hotta |first3=Seiji |title=2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) |chapter=Detection of Animal Behind Cages Using Convolutional Neural Network |chapter-url=https://ieeexplore.ieee.org/document/9158137 |year=2020 |___location=Phuket, Thailand |publisher=IEEE |pages=242–245 |doi=10.1109/ECTI-CON49241.2020.9158137 |isbn=978-1-7281-6486-1|s2cid=221086279 }}</ref><ref>{{Cite book |last1=Oishi |first1=Yu |last2=Matsunaga |first2=Tsuneo |title=2010 IEEE International Geoscience and Remote Sensing Symposium |chapter=Automatic detection of moving wild animals in airborne remote sensing images |chapter-url=https://ieeexplore.ieee.org/document/5654227 |year=2010 |pages=517–519 |doi=10.1109/IGARSS.2010.5654227|isbn=978-1-4244-9565-8 |s2cid=16812504 }}</ref><ref>{{Cite journal |last1=Ramanan |first1=D. |last2=Forsyth |first2=D.A. |last3=Barnard |first3=K. |title=Building models of animals from video |url=https://ieeexplore.ieee.org/document/1642665 |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |year=2006 |volume=28 |issue=8 |pages=1319–1334 |doi=10.1109/TPAMI.2006.155 |pmid=16886866 |s2cid=1699015 |issn=0162-8828}}</ref>

* Fish detection<ref>{{Cite journal |title=Fish Detection Using Deep Learning |journal=Applied Computational Intelligence and Soft Computing |year=2020 |language=en |doi=10.1155/2020/3738108|doi-access=free |last1=Cui |first1=Suxia |last2=Zhou |first2=Yu |last3=Wang |first3=Yonghui |last4=Zhai |first4=Lujun |volume=2020 |pages=1–13 }}</ref>