Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects. This paper proposes a new SOD dataset consisting of 39,070 images including 137,121 bird instances, which is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The detail of the challenge with the SOD4SB dataset is introduced in this paper. In total, 223 participants joined this challenge. This paper briefly introduces the award-winning methods. The dataset, the baseline code, and the website for evaluation on the public testset are publicly available.

PDF Abstract

Datasets


Introduced in the Paper:

SOD4SB

Used in the Paper:

MS COCO

Results from the Paper


Ranked #2 on Small Object Detection on SOD4SB Public Test (using extra training data)

     Get a GitHub badge
Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Small Object Detection SOD4SB Private Test E2 method (Normalized Gaussian Wasserstein Distance + Switch Hard Augmentation + Multi scale train + Weight Moving Average + CenterNet + VarifocalNet) AP50 22.1 # 5
Small Object Detection SOD4SB Private Test DL method (YOLOv8 + Ensamble) AP50 22.9 # 3
Small Object Detection SOD4SB Public Test DL method (YOLOv8 + Ensamble) AP50 73.1 # 2
Small Object Detection SOD4SB Public Test E2 method (Normalized Gaussian Wasserstein Distance + Switch Hard Augmentation + Multi scale train + Weight Moving Average + CenterNet + VarifocalNet) AP50 69.6 # 5

Methods


No methods listed for this paper. Add relevant methods here