增强至关重要：一种用于噪声标注下X光违禁品检测的混合粘贴方法
Augmentation Matters: A Mix-Paste Method for X-Ray Prohibited Item Detection under Noisy Annotations
AI总结：
本文提出Mix-Paste标签感知混合粘贴增强方法及大损失抑制策略，通过在噪声标注下混合同类X光图像块模拟物品重叠，提升违禁品检测鲁棒性，并在X光数据集和噪声MS-COCO上验证了有效性。
摘要：
自动X光违禁品检测对公共安全至关重要。现有的基于深度学习的方法均假设训练X光图像的标注是正确的。然而，对于大规模X光图像而言，获得正确标注极其困难甚至不可能，因为物品重叠无处不在。因此，X光图像很容易被噪声标注污染，导致现有方法的性能下降。在本文中，我们从数据增强的新视角出发，解决在噪声标注（包括类别噪声和边界框噪声）下训练鲁棒违禁品检测器这一具有挑战性的问题，并提出一种有效的标签感知混合图像块粘贴增强方法（Mix-Paste）。具体而言，对于每个物品图像块，我们将来自不同图像且具有相同类别标签的多个物品图像块进行混合，并用混合后的图像块替换图像中的原始图像块。通过这种方式，生成图像中包含正确违禁品的概率得以提高。同时，混合过程模拟了物品重叠，使模型能够学习X光图像的特征。此外，我们设计了一种基于物品的大损失抑制（LLS）策略，以抑制由于混合操作而可能对额外物品产生潜在正预测所对应的大损失。我们在噪声标注下的X光数据集上展示了所提方法的优越性。此外，我们在噪声MS-COCO数据集上评估了我们的方法，以展示其泛化能力。这些结果清楚地表明数据增强在处理噪声标注方面具有巨大潜力。源代码已发布在 https://github.com/wscds/Mix-Paste。
英文摘要：
Automatic X-ray prohibited item detection is vital for public safety. Existing deep learning-based methods all assume that the annotations of training X-ray images are correct. However, obtaining correct annotations is extremely hard if not impossible for large-scale X-ray images, where item overlapping is ubiquitous.As a result, X-ray images are easily contaminated with noisy annotations, leading to performance deterioration of existing methods.In this paper, we address the challenging problem of training a robust prohibited item detector under noisy annotations (including both category noise and bounding box noise) from a novel perspective of data augmentation, and propose an effective label-aware mixed patch paste augmentation method (Mix-Paste). Specifically, for each item patch, we mix several item patches with the same category label from different images and replace the original patch in the image with the mixed patch. In this way, the probability of containing the correct prohibited item within the generated image is increased. Meanwhile, the mixing process mimics item overlapping, enabling the model to learn the characteristics of X-ray images. Moreover, we design an item-based large-loss suppression (LLS) strategy to suppress the large losses corresponding to potentially positive predictions of additional items due to the mixing operation. We show the superiority of our method on X-ray datasets under noisy annotations. In addition, we evaluate our method on the noisy MS-COCO dataset to showcase its generalization ability. These results clearly indicate the great potential of data augmentation to handle noise annotations. The source code is released at https://github.com/wscds/Mix-Paste.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
