Survey of small object detection

Author:

Xiaoying Pan, ,Ningxing Jia,Yuanzhen Mu,Xuanrong Gao

Publisher

Aerospace Information Research Institute, Chinese Academy of Sciences

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Human-Computer Interaction

Reference88 articles.

1. Akyon F C,Altinuc S O and Temizel A. 2022. Slicing aided hyper inference and fine-tuning for small object detection. IEEE International Conference on Image Processing[EB/OL].[2022-10-24].http://arxiv.org/pdf/2202.06934.pdf

2. Bai Y C,Zhang Y Q,Ding M L and Ghanem B. 2018. SOD-MTGAN:small object detection via multi-task generative adversarial network//Proceedings of the 15th European Conference on Computer Vision(ECCV). Munich,Germany:Springer:210-226[DOI:10. 1007/978-3-030-01261-8_13]

3. Benjumea A,Teeti I,Cuzzolin F and Bradley A. 2021. YOLO-Z:improving small object detection in YOLOv5 for autonomous vehicles//Proceedings of the International Conference on Computer Vision(ICCV 2021):The ROAD Challenge Workshop.[s. l.]:[s.n.]

4. Bianco S,Buzzelli M,Mazzini D and Schettini R. 2017. Deep learning for logo recognition. Neurocomputing,245:23-30[DOI:10.1016/j.neucom.2017.03.051]

5. Bochkovskiy A,Wang C Y and Liao H Y M. 2020. Yolov4:Optimal speed and accuracy of object detection. Computer Vision and Pattern Recognition[EB/OL].[2020-04-23].http://arxiv.org/pdf/2004.10934.pdf

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