no code implementations • 27 May 2024 • Yang Zhang, Mingying Li, Huilin Pan, Moyun Liu, Yang Zhou
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.
1 code implementation • 10 Dec 2023 • Yang Zhang, Huilin Pan, Mingying Li, An Wang, Yang Zhou, Hongliang Ren
Existing modeling shortcomings of spatial invariance and pooling layers in conventional CNNs often ignore the neglect of crucial global information, resulting in error localization for fault objection tasks of freight trains.
no code implementations • 3 Jul 2023 • Yang Zhang, Huilin Pan, Yang Zhou, Mingying Li, Guodong Sun
Efficient visual fault detection of freight trains is a critical part of ensuring the safe operation of railways under the restricted hardware environment.