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.

我们提出了一种基于高效的神经架构搜索（NAS）框架，用于货车的视觉故障检测，以搜索具有多尺度表示能力的特定任务检测头，通过设计具有鲁棒性的数据容量降低搜索成本，并提出了一种新的共享策略来减少内存并进一步提高搜索效率，实验结果证明了我们方法的有效性，实现了分别 46.8 和 47.9 mAP 的 Bottom View 和 Side View 数据集，超越了现有方法，并随着数据容量的减少而线性降低了搜索成本。