Detecting small and distant objects remains challenging for object detectors due to scale variation, low resolution, and background clutter. Safety-critical applications require reliable detection of these objects for safe planning. Depth information can improve detection, but existing approaches require complex, model-specific architectural modifications. We provide a theoretical analysis followed by an empirical investigation of the depth-detection relationship. Together, they explain how depth causes systematic performance degradation and why depth-informed supervision mitigates it. We introduce DepthPrior, a framework that uses depth as prior knowledge rather than as a fused feature, providing comparable benefits without modifying detector architectures. DepthPrior consists of Depth-Based Loss Weighting (DLW) and Depth-Based Loss Stratification (DLS) during training, and Depth-Aware Confidence Thresholding (DCT) during inference. The only overhead is the initial cost of depth estimation. Experiments across four benchmarks (KITTI, MS COCO, VisDrone, SUN RGB-D) and two detectors (YOLOv11, EfficientDet) demonstrate the effectiveness of DepthPrior, achieving up to +9% mAP$_S$ and +7% mAR$_S$ for small objects, with inference recovery rates as high as 95:1 (true vs. false detections). DepthPrior offers these benefits without additional sensors, architectural changes, or performance costs. Code is available at https://github.com/mos-ks/DepthPrior.

翻译：检测小型和远距离目标对于目标检测器而言仍然具有挑战性，这主要归因于尺度变化、低分辨率以及背景干扰。在安全关键型应用中，可靠检测此类目标对于安全规划至关重要。深度信息能够提升检测性能，但现有方法需要进行复杂且模型特定的架构修改。本文首先提供理论分析，随后对深度与检测之间的关系进行实证研究。二者共同阐释了深度如何导致系统性性能下降，以及为何基于深度的监督能够缓解这一问题。我们提出DepthPrior框架，该框架将深度作为先验知识而非融合特征使用，在不修改检测器架构的前提下提供可比的性能增益。DepthPrior包含训练阶段的基于深度的损失加权（DLW）与基于深度的损失分层（DLS），以及推理阶段的深度感知置信度阈值（DCT）。其唯一开销仅为深度估计的初始计算成本。在四个基准数据集（KITTI、MS COCO、VisDrone、SUN RGB-D）和两种检测器（YOLOv11、EfficientDet）上的实验证明了DepthPrior的有效性，对于小目标检测实现了高达+9%的mAP$_S$与+7%的mAR$_S$提升，推理阶段的真阳性与假阳性检测恢复比最高可达95:1。DepthPrior无需额外传感器、架构改动或性能代价即可获得这些优势。代码发布于https://github.com/mos-ks/DepthPrior。