  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.
