[Paper Review] Augmentation for small object detection

Máté Kisantal, Zbigniew Wojna+3 more|arXiv (Cornell University)|Feb 19, 2019

Advanced Neural Network Applications24 references62 citations

TL;DR

The paper identifies that small objects are poorly matched to anchors in Mask R-CNN on MS COCO and proposes oversampling and copy-paste augmentation of small objects to improve small-object detection and segmentation, achieving notable gains over Mask R-CNN.

ABSTRACT

In recent years, object detection has experienced impressive progress. Despite these improvements, there is still a significant gap in the performance between the detection of small and large objects. We analyze the current state-of-the-art model, Mask-RCNN, on a challenging dataset, MS COCO. We show that the overlap between small ground-truth objects and the predicted anchors is much lower than the expected IoU threshold. We conjecture this is due to two factors; (1) only a few images are containing small objects, and (2) small objects do not appear enough even within each image containing them. We thus propose to oversample those images with small objects and augment each of those images by copy-pasting small objects many times. It allows us to trade off the quality of the detector on large objects with that on small objects. We evaluate different pasting augmentation strategies, and ultimately, we achieve 9.7\% relative improvement on the instance segmentation and 7.1\% on the object detection of small objects, compared to the current state of the art method on MS COCO.

Motivation & Objective

• Explain why small object detection lags behind larger objects in MS COCO with Mask R-CNN.
• Propose oversampling and copy-paste augmentation to boost small-object performance.
• Evaluate augmentation strategies and quantify trade-offs between small and large object performance.

Proposed method

• Analyze anchor matching and small-object coverage in Mask R-CNN on MS COCO.
• Oversample images containing small objects during training.
• Augment images by copy-pasting small object instances with random transformations.
• Test different copy-paste strategies and pasting algorithms to assess impact on AP for small objects.
• Compare small-object performance against the baseline Mask R-CNN on MS COCO.

Experimental results

Research questions

• RQ1 How does anchor-based matching affect small object detection in Mask R-CNN on MS COCO?
• RQ2 What is the impact of oversampling images with small objects on small-object AP (segmentation and detection)?
• RQ3 Can copy-paste augmentation of small objects improve small-object AP without severely harming large-object AP?
• RQ4 Which copy-paste strategies and pasting rules maximize small-object AP?

Key findings

• Oversampling images with small objects yields a small-object AP improvement (up to 1 percentage point in segmentation AP, ~8.85% relative gain).
• Copy-paste augmentation of small objects increases small-object AP and overall AP when combined with oversampling, with best results from a mix of original and augmented images at a 2:1 ratio.
• Copy-pasting a single small object a few times (1–3x) often yields better small-object gains than pasting many copies of all small objects.
• Avoid overlapping pasted objects and do not apply edge blur; non-overlapping pastes preserve small-object gains.
• Best overall results arise from combining oversampling with moderate augmentation (original+aug) at p=0.5 and original:augmented small-object ratio 2:1.
• The augmentation strategy yields a trade-off: improvements for small objects may come at the expense of large-object performance.