Small object detection using bounding boxes guided by confidences with C-BBL

Small object detection using bounding boxes guided by confidences with C-BBL

Confidence-driven Bounding Box Localization for Small Object Detection

arXiv paper abstract https://arxiv.org/abs/2303.01803

arXiv PDF paper https://arxiv.org/pdf/2303.01803.pdf

Despite advancements in generic object detection, there remains a performance gap in detecting small objects compared to normal-scale objects.

... observe that existing bounding box regression methods tend to produce distorted gradients for small objects and result in less accurate localization.

... 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, ... introduce a classification-based localization objective through cross entropy between ground truth and predicted confidence distribution, generating confidence-driven gradients.

... The method is evaluated on multiple detectors using three object detection benchmarks and consistently improves baseline detectors, achieving state-of-the-art performance ...

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