The quality of training datasets for deep neural networks is a key factor contributing to the accuracy of resulting models. This effect is amplified in difficult tasks such as object detection. Dealing with errors in datasets is often limited to accepting that some fraction of examples are incorrect, estimating their confidence, and either assigning appropriate weights or ignoring uncertain ones during training. In this work, we propose a different approach. We introduce the Confident Learning for Object Detection (CLOD) algorithm for assessing the quality of each label in object detection datasets, identifying missing, spurious, mislabeled, and mislocated bounding boxes and suggesting corrections. By focusing on finding incorrect examples in the training datasets, we can eliminate them at the root. Suspicious bounding boxes can be reviewed to improve the quality of the dataset, leading to better models without further complicating their already complex architectures. The proposed method is able to point out nearly 80% of artificially disturbed bounding boxes with a false positive rate below 0.1. Cleaning the datasets by applying the most confident automatic suggestions improved mAP scores by 16% to 46%, depending on the dataset, without any modifications to the network architectures. This approach shows promising potential in rectifying state-of-the-art object detection datasets.

翻译：深度神经网络训练数据集质量是影响模型精度的关键因素，这一效应在目标检测等复杂任务中尤为明显。当前处理数据集错误的方法通常局限于承认部分样本存在错误，通过估计其置信度，在训练过程中对不确定样本赋予权重或直接忽略。本文提出了一种不同的方法，即引入面向目标检测的置信学习（CLOD）算法，用于评估目标检测数据集中每个标签的质量，识别缺失、冗余、错误标注及定位错误的边界框，并给出修正建议。通过聚焦训练数据集中错误样本的识别，可从根源上消除这些问题。可疑的边界框经审查后，可在不进一步复杂化已有复杂网络架构的前提下提升数据集质量，从而优化模型性能。实验表明，该方法能以低于0.1的假阳性率识别近80%的人工扰动边界框。应用置信度最高的自动修正建议对数据集进行清洗后，无需修改网络架构即可使mAP分数根据不同数据集提升16%至46%。该方案在纠正现有先进目标检测数据集方面展现出显著潜力。