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Small object detection

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Small object detection is a particular case of object detection where various techniques are employed to detect small objects in digital images and videos. "Small objects" are objects having a small pixel footprint in the input image. In areas such as aerial imagery, state-of-the-art object detection techniques under performed because of small objects.

Uses

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An example of object tracking

Small object detection has applications in various fields such as Video surveillance (Traffic video Surveillance,[1][2] Small object retrieval,[3][4] Anomaly detection,[5] Maritime surveillance, Drone surveying, Traffic flow analysis,[6] and Object tracking.

Problems with small objects

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  • Modern-day object detection algorithms such as You Only Look Once[7][8][9][10][11][12][13] heavily uses convolution layers to learn 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.[14] 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, drones are very widely used in aerial imagery.[15] They are equipped with hardware (sensors) and software (algorithms) 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.
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).
Shadow and drone movement effect

Methods

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Various methods[16] are available to detect small objects, which fall under three categories:

YOLOv5 detection result
YOLOv5 and SAHI interface
YOLOv7 detection output

Improvising existing techniques

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There are various ways to detect small objects with existing techniques. Some of them are mentioned below,

Choosing a data set that has small objects

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The machine learning model's output depends on "How well it is trained."[17] So, the data set must include small objects to detect such objects. Also, modern-day detectors, such as YOLO, rely on anchors. Latest versions of YOLO (starting from YOLOv5[18]) 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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Deep learning models have billions of neurons that settle down to some weights after training. Therefore, it requires a good amount of quantitative and qualitative data for better training.[19] Data augmentation is useful technique to generate more diverse data[17] from an existing data set.

Increasing image capture resolution and model’s input resolution

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These help to get more features from objects and eventually learn the best from them. For example, a bike object in the 1280 X 1280 resolution image has more features than the 640 X 640 resolution.

Auto learning anchors

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Selecting anchor size plays a vital role in small object detection.[20] Instead of hand picking it, use algorithms that identify it based on the data set. YOLOv5 uses a K-means algorithm to define anchor size.

Tiling approach during training and inference

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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[21] 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)

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Use a feature pyramid network[22] to learn features at a multi-scale: e.g., Twin Feature Pyramid Networks (TFPN),[23] Extended Feature Pyramid Network (EFPN).[24] FPN helps to sustain features of small objects against convolution layers.

Add-on techniques

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Instead of modifying existing methods, some add-on techniques are there, which can be directly placed on top of existing approaches to detect smaller objects. One such technique is Slicing Aided Hyper Inference(SAHI).[25] The image is sliced into different-sized multiple overlapping patches. Hyper-parameters define their dimensions. Then patches are resized, while maintaining the aspect ratio during fine-tuning. These patches are then provided for training the model.

Well-Optimised techniques for small object detection

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Various deep learning techniques are available that focus on such object detection problems: e.g., Feature-Fused SSD,[26] YOLO-Z.[27] Such methods work on "How to sustain features of small objects while they pass through convolution networks."

Other applications

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See also

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References

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  1. ^ Saran K B; Sreelekha G (2015). "Traffic video surveillance: Vehicle detection and classification". 2015 International Conference on Control Communication & Computing India (ICCC). Trivandrum, Kerala, India: IEEE. pp. 516–521. doi:10.1109/ICCC.2015.7432948. ISBN 978-1-4673-7349-4. S2CID 14779393.
  2. ^ Nemade, Bhushan (2016-01-01). "Automatic Traffic Surveillance Using Video Tracking". Procedia Computer Science. Proceedings of International Conference on Communication, Computing and Virtualization (ICCCV) 2016. 79: 402–409. doi:10.1016/j.procs.2016.03.052. ISSN 1877-0509.
  3. ^ Guo, Haiyun; Wang, Jinqiao; Xu, Min; Zha, Zheng-Jun; Lu, Hanqing (2015-10-13). "Learning Multi-view Deep Features for Small Object Retrieval in Surveillance Scenarios". Proceedings of the 23rd ACM international conference on Multimedia. MM '15. New York, NY, USA: Association for Computing Machinery. pp. 859–862. doi:10.1145/2733373.2806349. ISBN 978-1-4503-3459-4. S2CID 9041849.
  4. ^ Galiyawala, Hiren; Raval, Mehul S.; Patel, Meet (2022-05-20). "Person retrieval in surveillance videos using attribute recognition". Journal of Ambient Intelligence and Humanized Computing. doi:10.1007/s12652-022-03891-0. ISSN 1868-5145. S2CID 248951090.
  5. ^ Ingle, Palash Yuvraj; Kim, Young-Gab (2022-05-19). "Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities". Sensors. 22 (10): 3862. Bibcode:2022Senso..22.3862I. doi:10.3390/s22103862. ISSN 1424-8220. PMC 9143895. PMID 35632270.
  6. ^ Tsuboi, Tsutomu; Yoshikawa, Noriaki (2020-03-01). "Traffic flow analysis in Ahmedabad (India)". Case Studies on Transport Policy. 8 (1): 215–228. doi:10.1016/j.cstp.2019.06.001. ISSN 2213-624X. S2CID 195543435.
  7. ^ Redmon, Joseph; Divvala, Santosh; Girshick, Ross; Farhadi, Ali (2016-05-09). "You Only Look Once: Unified, Real-Time Object Detection". arXiv:1506.02640 [cs.CV].
  8. ^ Redmon, Joseph; Farhadi, Ali (2016-12-25). "YOLO9000: Better, Faster, Stronger". arXiv:1612.08242 [cs.CV].
  9. ^ Redmon, Joseph; Farhadi, Ali (2018-04-08). "YOLOv3: An Incremental Improvement". arXiv:1804.02767 [cs.CV].
  10. ^ Bochkovskiy, Alexey; Wang, Chien-Yao; Liao, Hong-Yuan Mark (2020-04-22). "YOLOv4: Optimal Speed and Accuracy of Object Detection". arXiv:2004.10934 [cs.CV].
  11. ^ Wang, Chien-Yao; Bochkovskiy, Alexey; Liao, Hong-Yuan Mark (2021-02-21). "Scaled-YOLOv4: Scaling Cross Stage Partial Network". arXiv:2011.08036 [cs.CV].
  12. ^ Li, Chuyi; Li, Lulu; Jiang, Hongliang; Weng, Kaiheng; Geng, Yifei; Li, Liang; Ke, Zaidan; Li, Qingyuan; Cheng, Meng; Nie, Weiqiang; Li, Yiduo; Zhang, Bo; Liang, Yufei; Zhou, Linyuan; Xu, Xiaoming (2022-09-07). "YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications". arXiv:2209.02976 [cs.CV].
  13. ^ Wang, Chien-Yao; Bochkovskiy, Alexey; Liao, Hong-Yuan Mark (2022-07-06). "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors". arXiv:2207.02696 [cs.CV].
  14. ^ Zhang, Mingrui; Zhao, Wenbing; Li, Xiying; Wang, Dan (2020-12-11). "Shadow Detection of Moving Objects in Traffic Monitoring Video". 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). Vol. 9. Chongqing, China: IEEE. pp. 1983–1987. doi:10.1109/ITAIC49862.2020.9338958. ISBN 978-1-7281-5244-8. S2CID 231824327.
  15. ^ "Interactive workshop "How drones are changing the world we live in"". 2016 Integrated Communications Navigation and Surveillance (ICNS). Herndon, VA: IEEE. 2016. pp. 1–17. doi:10.1109/ICNSURV.2016.7486437. ISBN 978-1-5090-2149-9. S2CID 21388151.
  16. ^ Nguyen, Nhat-Duy; Do, Tien; Ngo, Thanh Duc; Le, Duy-Dinh (2020). "An Evaluation of Deep Learning Methods for Small Object Detection". Journal of Electrical and Computer Engineering. 2020: 1–18. doi:10.1155/2020/3189691.
  17. ^ a b Gong, Zhiqiang; Zhong, Ping; Hu, Weidong (2019). "Diversity in Machine Learning". IEEE Access. 7: 64323–64350. arXiv:1807.01477. doi:10.1109/ACCESS.2019.2917620. ISSN 2169-3536. S2CID 206491718.
  18. ^ Jocher, Glenn; Chaurasia, Ayush; Stoken, Alex; Borovec, Jirka; NanoCode012; Kwon, Yonghye; TaoXie; Michael, Kalen; Fang, Jiacong (2022-08-17). "ultralytics/yolov5: v6.2 - YOLOv5 Classification Models, Apple M1, Reproducibility, ClearML and Deci.ai integrations". doi:10.5281/zenodo.3908559. Retrieved 2022-09-14. {{cite journal}}: Cite journal requires |journal= (help)CS1 maint: numeric names: authors list (link)
  19. ^ "The Size and Quality of a Data Set | Machine Learning". Google Developers. Retrieved 2022-09-14.
  20. ^ Zhong, Yuanyi; Wang, Jianfeng; Peng, Jian; Zhang, Lei (2020-01-26). "Anchor Box Optimization for Object Detection". arXiv:1812.00469 [cs.CV].
  21. ^ Unel, F. Ozge; Ozkalayci, Burak O.; Cigla, Cevahir (2019). "The Power of Tiling for Small Object Detection". 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Long Beach, CA, USA: IEEE. pp. 582–591. doi:10.1109/CVPRW.2019.00084. ISBN 978-1-7281-2506-0. S2CID 198903617.
  22. ^ Lin, Tsung-Yi; Dollár, Piotr; Girshick, Ross; He, Kaiming; Hariharan, Bharath; Belongie, Serge (2017-04-19). "Feature Pyramid Networks for Object Detection". arXiv:1612.03144 [cs.CV].
  23. ^ Liang, Yi; Changjian, Wang; Fangzhao, Li; Yuxing, Peng; Qin, Lv; Yuan, Yuan; Zhen, Huang (2019). "TFPN: Twin Feature Pyramid Networks for Object Detection". 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). Portland, OR, USA: IEEE. pp. 1702–1707. doi:10.1109/ICTAI.2019.00251. ISBN 978-1-7281-3798-8. S2CID 211211764.
  24. ^ Deng, Chunfang; Wang, Mengmeng; Liu, Liang; Liu, Yong (2020-04-09). "Extended Feature Pyramid Network for Small Object Detection". arXiv:2003.07021 [cs.CV].
  25. ^ Akyon, Fatih Cagatay; Altinuc, Sinan Onur; Temizel, Alptekin (2022-07-12). "Slicing Aided Hyper Inference and Fine-Tuning for Small Object Detection". 2022 IEEE International Conference on Image Processing (ICIP). pp. 966–970. arXiv:2202.06934. doi:10.1109/ICIP46576.2022.9897990. ISBN 978-1-6654-9620-9. S2CID 246823962.
  26. ^ Cao, Guimei; Xie, Xuemei; Yang, Wenzhe; Liao, Quan; Shi, Guangming; Wu, Jinjian (2018-04-10). "Feature-fused SSD: Fast detection for small objects". In Dong, Junyu; Yu, Hui (eds.). Ninth International Conference on Graphic and Image Processing (ICGIP 2017). Vol. 10615. SPIE. pp. 381–388. arXiv:1709.05054. Bibcode:2018SPIE10615E..1EC. doi:10.1117/12.2304811. ISBN 9781510617414. S2CID 20592770.
  27. ^ Benjumea, Aduen; Teeti, Izzeddin; Cuzzolin, Fabio; Bradley, Andrew (2021-12-23). "YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles". arXiv:2112.11798 [cs.CV].
  28. ^ Rajendran, Logesh; Shyam Shankaran, R (2021). "Bigdata Enabled Realtime Crowd Surveillance Using Artificial Intelligence and Deep Learning". 2021 IEEE International Conference on Big Data and Smart Computing (BigComp). Jeju Island, Korea (South): IEEE. pp. 129–132. doi:10.1109/BigComp51126.2021.00032. ISBN 978-1-7281-8924-6. S2CID 232236614.
  29. ^ Sivachandiran, S.; Mohan, K. Jagan; Nazer, G. Mohammed (2022-03-29). "Deep Transfer Learning Enabled High-Density Crowd Detection and Classification using Aerial Images". 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). Erode, India: IEEE. pp. 1313–1317. doi:10.1109/ICCMC53470.2022.9753982. ISBN 978-1-6654-1028-1. S2CID 248131806.
  30. ^ Santhini, C.; Gomathi, V. (2018). "Crowd Scene Analysis Using Deep Learning Network". 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT). pp. 1–5. doi:10.1109/ICCTCT.2018.8550851. ISBN 978-1-5386-3702-9. S2CID 54438440.
  31. ^ Sharath, S.V.; Biradar, Vidyadevi; Prajwal, M.S.; Ashwini, B. (2021-11-19). "Crowd Counting in High Dense Images using Deep Convolutional Neural Network". 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). Nitte, India: IEEE. pp. 30–34. doi:10.1109/DISCOVER52564.2021.9663716. ISBN 978-1-6654-1244-5. S2CID 245707782.
  32. ^ Wang, Hongbo; Hou, Jiaying; Chen, Na (2019). "A Survey of Vehicle Re-Identification Based on Deep Learning". IEEE Access. 7: 172443–172469. doi:10.1109/ACCESS.2019.2956172. ISSN 2169-3536. S2CID 209319743.
  33. ^ Santhanam, Sanjay; B, Sudhir Sidhaarthan; Panigrahi, Sai Sudha; Kashyap, Suryakant Kumar; Duriseti, Bhargav Krishna (2021-11-26). "Animal Detection for Road safety using Deep Learning". 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA). Nagpur, India: IEEE. pp. 1–5. doi:10.1109/ICCICA52458.2021.9697287. ISBN 978-1-6654-2040-2. S2CID 246663727.
  34. ^ Li, Nopparut; Kusakunniran, Worapan; Hotta, Seiji (2020). "Detection of Animal Behind Cages Using Convolutional Neural Network". 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). Phuket, Thailand: IEEE. pp. 242–245. doi:10.1109/ECTI-CON49241.2020.9158137. ISBN 978-1-7281-6486-1. S2CID 221086279.
  35. ^ Oishi, Yu; Matsunaga, Tsuneo (2010). "Automatic detection of moving wild animals in airborne remote sensing images". 2010 IEEE International Geoscience and Remote Sensing Symposium. pp. 517–519. doi:10.1109/IGARSS.2010.5654227. ISBN 978-1-4244-9565-8. S2CID 16812504.
  36. ^ Ramanan, D.; Forsyth, D.A.; Barnard, K. (2006). "Building models of animals from video". IEEE Transactions on Pattern Analysis and Machine Intelligence. 28 (8): 1319–1334. doi:10.1109/TPAMI.2006.155. ISSN 0162-8828. PMID 16886866. S2CID 1699015.
  37. ^ Cui, Suxia; Zhou, Yu; Wang, Yonghui; Zhai, Lujun (2020). "Fish Detection Using Deep Learning". Applied Computational Intelligence and Soft Computing. 2020: 1–13. doi:10.1155/2020/3738108.
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  • VisDrone dataset by AISKYEYE team at Lab of Machine Learning and Data Mining, Tianjin University, China.
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