Efficient Visual Fault Detection for Freight Train via Neural Architecture Search with Data Volume Robustness

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.

Spatial-wise Dynamic Distillation for MLP-like Efficient Visual Fault Detection of Freight Trains

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.

Fault Detection object-detection +1

Efficient Visual Fault Detection for Freight Train Braking System via Heterogeneous Self Distillation in the Wild

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.

Fault Detection object-detection +1