Small object detection: Difference between revisions

Revision as of 04:11, 26 September 2022

Revision as of 04:12, 26 September 2022

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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}}</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>{{Citationcite document |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 ====