AI论文

基于深度学习的故障检测方法已经取得了显著的成功。在货运列车视觉故障检测中，跨类别组件（尺度方差）之间的特征差异很大，但相反，在同一类别内，这会导致检测器的尺度意识。此外，任务特定网络的设计很大程度上依赖于人类专业知识。因此，由于其具有显著的性能，神经架构搜索（NAS）受到了很大的关注。然而，由于搜索空间巨大，数据量巨大，NAS 计算密集型。

在这项工作中，我们提出了一个高效的基于 NAS 的视觉列车故障检测框架，以寻找具有多尺度表示能力的任务特定检测头。首先，我们设计了一个基于尺度的搜索空间，用于在头部发现有效的接收区域。其次，我们探讨了数据量对搜索成本的影响，并提出了一个新的共享策略来降低内存并进一步提高搜索效率。大量实验结果证明，我们的方法在数据量鲁棒性方面具有有效性，在bottom view和side view数据集上分别获得了46.8和47.9 mAP。我们的框架超过了最先进的 approaches，并且随着数据量的减少，搜索成本线性降低。

Deep learning-based fault detection methods have achieved significant success. In visual fault detection of freight trains, there exists a large characteristic difference between inter-class components (scale variance) but intra-class on the contrary, which entails scale-awareness for detectors. Moreover, the design of task-specific networks heavily relies on human expertise. As a consequence, neural architecture search (NAS) that automates the model design process gains considerable attention because of its promising performance. However, NAS is computationally intensive due to the large search space and huge data volume. In this work, we propose an efficient NAS-based framework for visual fault detection of freight trains to search for the task-specific detection head with capacities of multi-scale representation. First, we design a scale-aware search space for discovering an effective receptive field in the head. Second, we explore the robustness of data volume to reduce search costs based on the specifically designed search space, and a novel sharing strategy is proposed to reduce memory and further improve search efficiency. Extensive experimental results demonstrate the effectiveness of our method with data volume robustness, which achieves 46.8 and 47.9 mAP on the Bottom View and Side View datasets, respectively. Our framework outperforms the state-of-the-art approaches and linearly decreases the search costs with reduced data volumes.

https://arxiv.org/abs/2405.17004

https://arxiv.org/pdf/2405.17004.pdf