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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 journalbook |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 |journal=2015 International Conference on Control Communication & Computing India (ICCC) |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}}</ref> [[Content-based image retrieval|Small object retrieval]],<ref>{{Cite journal |last1=Guo |first1=Haiyun |last2=Wang |first2=Jinqiao |last3=Xu |first3=Min |last4=Zha |first4=Zheng-Jun |last5=Lu |first5=Hanqing |date=2015-10-13 |title=Learning Multi-view Deep Features for Small Object Retrieval in Surveillance Scenarios |url=https://doi.org/10.1145/2733373.2806349 |journal=Proceedings of the 23rd ACM International Conference on Multimedia |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}}</ref> and [[Video tracking|Object tracking]].
== Problems with small objects ==
* Modern-day object detection algorithms such as You Only Look Once(YOLO)<ref>{{cite arXiv |last1=Redmon |first1=Joseph |last2=Divvala |first2=Santosh |last3=Girshick |first3=Ross |last4=Farhadi |first4=Ali |date=2016-05-09 |title=You Only Look Once: Unified, Real-Time Object Detection |class=cs.CV |eprint=1506.02640}}</ref><ref>{{cite arXiv |last1=Redmon |first1=Joseph |last2=Farhadi |first2=Ali |date=2016-12-25 |title=YOLO9000: Better, Faster, Stronger |class=cs.CV |eprint=1612.08242}}</ref><ref>{{cite arXiv |last1=Redmon |first1=Joseph |last2=Farhadi |first2=Ali |date=2018-04-08 |title=YOLOv3: An Incremental Improvement |class=cs.CV |eprint=1804.02767}}</ref><ref>{{cite arXiv |last1=Bochkovskiy |first1=Alexey |last2=Wang |first2=Chien-Yao |last3=Liao |first3=Hong-Yuan Mark |date=2020-04-22 |title=YOLOv4: Optimal Speed and Accuracy of Object Detection |class=cs.CV |eprint=2004.10934}}</ref><ref>{{cite arXiv |last1=Wang |first1=Chien-Yao |last2=Bochkovskiy |first2=Alexey |last3=Liao |first3=Hong-Yuan Mark |date=2021-02-21 |title=Scaled-YOLOv4: Scaling Cross Stage Partial Network |class=cs.CV |eprint=2011.08036}}</ref><ref>{{cite arXiv |last1=Li |first1=Chuyi |last2=Li |first2=Lulu |last3=Jiang |first3=Hongliang |last4=Weng |first4=Kaiheng |last5=Geng |first5=Yifei |last6=Li |first6=Liang |last7=Ke |first7=Zaidan |last8=Li |first8=Qingyuan |last9=Cheng |first9=Meng |last10=Nie |first10=Weiqiang |last11=Li |first11=Yiduo |last12=Zhang |first12=Bo |last13=Liang |first13=Yufei |last14=Zhou |first14=Linyuan |last15=Xu |first15=Xiaoming |date=2022-09-07 |title=YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications |class=cs.CV |eprint=2209.02976}}</ref><ref>{{cite arXiv |last1=Wang |first1=Chien-Yao |last2=Bochkovskiy |first2=Alexey |last3=Liao |first3=Hong-Yuan Mark |date=2022-07-06 |title=YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors |class=cs.CV |eprint=2207.02696}}</ref> heavily uses convolution layers to learn [[Feature (computer vision)|features]]. As an object passes through convolution layers, its size gets reduced. Therefore, the small object disappears after several layers and becomes undetectable.
* Sometimes, the shadow of an object is detected as a part of object itself.<ref>{{Cite journalbook |last1=Zhang |first1=Mingrui |last2=Zhao |first2=Wenbing |last3=Li |first3=Xiying |last4=Wang |first4=Dan |datetitle=2020-12-11 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) |titlechapter=Shadow Detection Ofof Moving Objects Inin Traffic Monitoring Video |date=2020-12-11 |chapter-url=https://ieeexplore.ieee.org/document/9338958 |journal=2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) |volume=9 |___location=Chongqing, China |publisher=IEEE |pages=1983–1987 |doi=10.1109/ITAIC49862.2020.9338958 |isbn=978-1-7281-5244-8|s2cid=231824327 }}</ref> So, the placement of the bounding box tends to centre around a shadow rather than an object. In the case of vehicle detection, [[pedestrian]] and two-wheeler detection suffer because of this.
* At present, [[Unmanned aerial vehicle|drones]] are very widely used in aerial imagery.<ref>{{Cite journal |title=Interactive workshop "How drones are changing the world we live in"book |chapter-url=https://ieeexplore.ieee.org/document/7486437 |journal=2016 Integrated Communications Navigation and Surveillance (ICNS) |year=2016 |___location=Herndon, VA |publisher=IEEE |pages=1–17 |doi=10.1109/ICNSURV.2016.7486437 |isbn=978-1-5090-2149-9|s2cid=21388151 |chapter=Interactive workshop "How drones are changing the world we live in" |title=2016 Integrated Communications Navigation and Surveillance (ICNS) }}</ref> They are equipped with hardware ([[sensor]]s) and software ([[algorithm]]s) that help maintain a particular stable position during their flight. In windy conditions, the drone automatically makes fine moves to maintain its position and that changes the view near the boundary. It may be possible that some new objects appear near the image boundary. Overall, these affect classification, detection, and eventually tracking accuracy.
[[File:Disp_shadow.jpg|thumb|Shadow and drone movement effect|alt=Here, both images are from same video. See, How the shadow of objects affecting detection accuracy. Also, drone's self-movement changes the scene near boundary(Refer to object "car" at bottom-left corner).]]
==== Tiling approach during training and inference ====
State-of-the-art object detectors allow only the fixed size of image and change the input image size according to it. This change may deform the small objects in the image. The tiling approach<ref>{{Cite journalbook |last1=Unel |first1=F. Ozge |last2=Ozkalayci |first2=Burak O. |last3=Cigla |first3=Cevahir |title=2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |chapter=The Power of Tiling for Small Object Detection |chapter-url=https://ieeexplore.ieee.org/document/9025422 |journal=2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |year=2019 |___location=Long Beach, CA, USA |publisher=IEEE |pages=582–591 |doi=10.1109/CVPRW.2019.00084 |isbn=978-1-7281-2506-0|s2cid=198903617 }}</ref> helps when an image has a high resolution than the model's fixed input size; instead of scaling it down, the image is broken down into tiles and then used in training. The same approach is used during inference as well.
==== Feature Pyramid Network (FPN) ====
Use a feature [[Pyramid (image processing)|pyramid]] network<ref>{{cite arXiv |last1=Lin |first1=Tsung-Yi |last2=Dollár |first2=Piotr |last3=Girshick |first3=Ross |last4=He |first4=Kaiming |last5=Hariharan |first5=Bharath |last6=Belongie |first6=Serge |date=2017-04-19 |title=Feature Pyramid Networks for Object Detection |class=cs.CV |eprint=1612.03144}}</ref> to learn features at a multi-scale: e.g., Twin Feature Pyramid Networks (TFPN),<ref>{{Cite journalbook |last1=Liang |first1=Yi |last2=Changjian |first2=Wang |last3=Fangzhao |first3=Li |last4=Yuxing |first4=Peng |last5=Qin |first5=Lv |last6=Yuan |first6=Yuan |last7=Zhen |first7=Huang |title=2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) |chapter=TFPN: Twin Feature Pyramid Networks for Object Detection |chapter-url=https://ieeexplore.ieee.org/document/8995365 |journal=2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) |year=2019 |___location=Portland, OR, USA |publisher=IEEE |pages=1702–1707 |doi=10.1109/ICTAI.2019.00251 |isbn=978-1-7281-3798-8|s2cid=211211764 }}</ref> Extended Feature Pyramid Network (EFPN).<ref>{{cite arXiv |last1=Deng |first1=Chunfang |last2=Wang |first2=Mengmeng |last3=Liu |first3=Liang |last4=Liu |first4=Yong |date=2020-04-09 |title=Extended Feature Pyramid Network for Small Object Detection |class=cs.CV |eprint=2003.07021}}</ref> FPN helps to sustain features of small objects against convolution layers.
=== Add-on techniques ===
== Other applications ==
* Crowd counting<ref>{{Cite journalbook |last1=Rajendran |first1=Logesh |last2=Shyam Shankaran |first2=R |title=2021 IEEE International Conference on Big Data and Smart Computing (Big ''Comp'') |chapter=Bigdata Enabled Realtime Crowd Surveillance Using Artificial Intelligence Andand Deep Learning |chapter-url=https://ieeexplore.ieee.org/document/9373133 |journal=2021 IEEE International Conference on Big Data and Smart Computing (BigComp) |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 journalbook |last1=Sivachandiran |first1=S. |last2=Mohan |first2=K. Jagan |last3=Nazer |first3=G. Mohammed |datetitle=2022-03-29 6th International Conference on Computing Methodologies and Communication (ICCMC) |titlechapter=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 |journal=2022 6th International Conference on Computing Methodologies and Communication (ICCMC) |___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 journalbook |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 |journal=2018 International Conference on Current Trends Towards Converging Technologies (ICCTCT) |year=2018 |pages=1–5 |doi=10.1109/ICCTCT.2018.8550851|isbn=978-1-5386-3702-9 |s2cid=54438440 }}</ref><ref>{{Cite journalbook |last1=Sharath |first1=S.V. |last2=Biradar |first2=Vidyadevi |last3=Prajwal |first3=M.S. |last4=Ashwini |first4=B. |datetitle=2021-11-19 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) |titlechapter=Crowd Counting in High Dense Images using Deep Convolutional Neural Network |date=2021-11-19 |chapter-url=https://ieeexplore.ieee.org/document/9663716 |journal=2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) |___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}}</ref>
* Animal detection<ref>{{Cite journalbook |last1=Santhanam |first1=Sanjay |last2=B |first2=Sudhir Sidhaarthan |last3=Panigrahi |first3=Sai Sudha |last4=Kashyap |first4=Suryakant Kumar |last5=Duriseti |first5=Bhargav Krishna |datetitle=2021-11-26 International Conference on Computational Intelligence and Computing Applications (ICCICA) |titlechapter=Animal Detection for Road safety using Deep Learning |date=2021-11-26 |chapter-url=https://ieeexplore.ieee.org/document/9697287 |journal=2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) |___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 journalbook |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 |journal=2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) |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 journalbook |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 |journal=2010 IEEE International Geoscience and Remote Sensing Symposium |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>
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