This paper investigates the integration of the Learning Using Privileged Information (LUPI) paradigm in object detection to exploit fine-grained, descriptive information available during training but not at inference. We introduce a general, model-agnostic methodology for injecting privileged information-such as bounding box masks, saliency maps, and depth cues-into deep learning-based object detectors through a teacher-student architecture. Experiments are conducted across five state-of-the-art object detection models and multiple public benchmarks, including UAV-based litter detection datasets and Pascal VOC 2012, to assess the impact on accuracy, generalization, and computational efficiency. Our results demonstrate that LUPI-trained students consistently outperform their baseline counterparts, achieving significant boosts in detection accuracy with no increase in inference complexity or model size. Performance improvements are especially marked for medium and large objects, while ablation studies reveal that intermediate weighting of teacher guidance optimally balances learning from privileged and standard inputs. The findings affirm that the LUPI framework provides an effective and practical strategy for advancing object detection systems in both resource-constrained and real-world settings.

翻译：本文研究了在目标检测中集成"利用特权信息学习"范式，以利用训练期间可用但推理时无法获得的细粒度描述性信息。我们提出了一种通用的、模型无关的方法论，通过师生架构将特权信息——如边界框掩码、显著性图和深度线索——注入基于深度学习的目标检测器中。实验在五种最先进的目标检测模型和多个公共基准数据集上进行，包括基于无人机的垃圾检测数据集和Pascal VOC 2012，以评估其对准确性、泛化能力和计算效率的影响。我们的结果表明，通过LUPI训练的学生模型始终优于其基线对应模型，在检测准确性上实现了显著提升，且未增加推理复杂度或模型规模。对于中型和大型目标的性能提升尤为明显，而消融研究表明，教师指导的中间加权能够最优地平衡从特权信息和标准输入中的学习。这些发现证实了LUPI框架为在资源受限和实际应用场景中推进目标检测系统提供了一种有效且实用的策略。