利用特权信息增强目标检测：一种模型无关的师生框架方法

摘要

本文研究如何将特权信息学习范式融入目标检测，通过利用训练阶段可获得的细粒度描述性信息（推理阶段不可用）来提升性能。我们提出一种与模型无关的通用方法，通过师生架构将边界框掩码、显著图、深度线索等特权信息注入基于深度学习的目标检测器。在五种先进目标检测模型和多个公共基准数据集（包括基于无人机的垃圾检测数据集和Pascal VOC 2012）上的实验表明，该方法能有效提升检测精度、泛化能力和计算效率。研究结果显示，经特权信息训练的学生模型始终优于基线模型，在未增加推理复杂度或模型参数量的情况下显著提高检测精度。其中中大型物体的性能提升尤为明显，消融实验表明对教师指导进行中间加权能最优平衡特权信息与标准输入的学习。本研究证实特权信息学习框架为资源受限环境和实际应用场景中的目标检测系统提供了一种高效实用的改进策略。

English

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.

利用特权信息增强目标检测：一种模型无关的师生框架方法

Enhancing Object Detection with Privileged Information: A Model-Agnostic Teacher-Student Approach

摘要

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