无需标注数据，通过检测框一致性与置信度评估目标检测模型性能
Automated Model Evaluation for Object Detection via Prediction Consistency and Reliability
- 利用检测器生成的候选框在NMS前后的空间一致性判断性能
- 在无真实标签情况下，比现有方法更准确估计检测性能
- 适用于快速评估模型在各种图像退化下的表现，适合工业部署
计算机视觉领域的进步使目标检测器训练更加高效，但其在实际应用中的性能评估仍依赖昂贵的人工标注。为此，我们提出一种自动化模型评估框架（AutoEval），引入预测一致性与可靠性（PCR）机制，利用传统检测器在非极大值抑制（NMS）前生成的多个候选边界框。PCR通过联合衡量两方面：1）NMS前后框的空间一致性；2）重叠框置信度反映保留框的可靠性，从而在无真实标签情况下估计检测性能。为实现更真实、可扩展的评估，我们构建了一个元数据集，包含不同严重程度的图像退化。实验表明，PCR比现有自动评估方法更准确，且覆盖更广的检测性能范围。代码已开源：https://github.com/YonseiML/autoeval-det。
原文摘要 · Abstract (English)
Recent advances in computer vision have made training object detectors more efficient and effective; however, assessing their performance in real-world applications still relies on costly manual annotation. To address this limitation, we develop an automated model evaluation (AutoEval) framework for object detection. We propose Prediction Consistency and Reliability (PCR), which leverages the multiple candidate bounding boxes that conventional detectors generate before non-maximum suppression (NMS). PCR estimates detection performance without ground-truth labels by jointly measuring 1) the spatial consistency between boxes before and after NMS, and 2) the reliability of the retained boxes via the confidence scores of overlapping boxes. For a more realistic and scalable evaluation, we construct a meta-dataset by applying image corruptions of varying severity. Experimental results demonstrate that PCR yields more accurate performance estimates than existing AutoEval methods, and the proposed meta-dataset covers a wider range of detection performance. The code is available at https://github.com/YonseiML/autoeval-det.
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