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.

翻译：尽管通用目标检测取得了进展，但小目标检测的性能仍与常规尺度目标存在差距。我们首次观察到现有边界框回归方法对小目标易产生畸变梯度，导致定位精度下降。为解决这一问题，我们提出了一种新颖的置信度驱动边界框定位方法（C-BBL）以修正梯度。C-BBL将连续标签离散化为网格，并构建双热点真值标签。在预测过程中，边界框头生成网格上的置信度分布。不同于传统检测器中的边界框回归范式，我们通过真值与预测置信度分布之间的交叉熵引入基于分类的定位损失，生成置信度驱动梯度。此外，C-BBL基于标签与预测的分布熵描述了不确定性损失，以进一步降低小目标定位的不确定性。该方法在三个目标检测基准上使用多种检测器进行评估，持续提升基线检测器性能，达到最先进水平。我们还验证了C-BBL对不同标签系统的泛化能力及其在高分辨率检测中的有效性，这证明了其作为通用解决方案的应用前景。