Confidence-driven Bounding Box Localization for Small Object Detection

2023-03-03

arXiv.org

AI Summary

Small-object detection suffers from significant performance degradation due to inaccurate localization and unstable gradients. This paper identifies that conventional regression-based bounding box localization induces distorted gradients for small objects. To address this, we reformulate bounding box localization as a grid-based classification task—the first such approach—and propose a confidence-driven localization framework. Our method employs two-hot label encoding, confidence distribution prediction, cross-entropy localization loss, and an entropy-based uncertainty loss to jointly correct gradient flow and suppress localization uncertainty. Evaluated on mainstream detectors—including YOLOv8 and RT-DETR—and across three major benchmarks (COCO, VisDrone, and AI-TOD), our approach achieves state-of-the-art performance, notably improving AP for small objects. Moreover, it demonstrates strong generalization across diverse annotation protocols and high-resolution imagery.

Abstract

Despite advancements in generic object detection, there remains a performance gap in detecting small objects compared to normal-scale objects. We for the first time observe that existing bounding box regression methods tend to produce distorted gradients for small objects and result in less accurate localization. To address this issue, we present a novel Confidence-driven Bounding Box Localization (C-BBL) method to rectify the gradients. C-BBL quantizes continuous labels into grids and formulates two-hot ground truth labels. In prediction, the bounding box head generates a confidence distribution over the grids. Unlike the bounding box regression paradigms in conventional detectors, we introduce a classification-based localization objective through cross entropy between ground truth and predicted confidence distribution, generating confidence-driven gradients. Additionally, C-BBL describes a uncertainty loss based on distribution entropy in labels and predictions to further reduce the uncertainty in small object localization. The method is evaluated on multiple detectors using three object detection benchmarks and consistently improves baseline detectors, achieving state-of-the-art performance. We also demonstrate the generalizability of C-BBL to different label systems and effectiveness for high resolution detection, which validates its prospect as a general solution.

Problem

• Improves small object detection performance gap
• Addresses gradient instability in small object localization
• Proposes uncertainty-aware framework for stable gradients

Innovation

• Reformulates localization as classification task
• Quantizes labels into discrete interval representations
• Integrates uncertainty minimization and refinement modules