Similarity Distance-Based Label Assignment for Tiny Object Detection

TL;DR

This paper introduces the SimD strategy for tiny object detection, which adaptively evaluates bounding box similarity considering location and shape, improving detection accuracy over existing methods.

Contribution

The paper proposes a novel similarity distance (SimD) method that adaptively learns hyperparameters and replaces IoU in label assignment for better tiny object detection.

Findings

Achieves 1.8 AP improvement on AI-TOD dataset.

Outperforms state-of-the-art methods in tiny object detection.

Demonstrates robustness across multiple datasets.

Abstract

Tiny object detection is becoming one of the most challenging tasks in computer vision because of the limited object size and lack of information. The label assignment strategy is a key factor affecting the accuracy of object detection. Although there are some effective label assignment strategies for tiny objects, most of them focus on reducing the sensitivity to the bounding boxes to increase the number of positive samples and have some fixed hyperparameters need to set. However, more positive samples may not necessarily lead to better detection results, in fact, excessive positive samples may lead to more false positives. In this paper, we introduce a simple but effective strategy named the Similarity Distance (SimD) to evaluate the similarity between bounding boxes. This proposed strategy not only considers both location and shape similarity but also learns hyperparameters adaptively, ensuring that it can adapt to different datasets and various object sizes in a dataset. Our approach can be simply applied in common anchor-based detectors in place of the IoU for label assignment and Non Maximum Suppression (NMS). Extensive experiments on four mainstream tiny object detection datasets demonstrate superior performance of our method, especially, 1.8 AP points and 4.1 AP points of very tiny higher than the state-of-the-art competitors on AI-TOD. Code is available at: \url{https://github.com/cszzshi/SimD}.