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Efficient Visual Fault Detection for Freight Train Braking System via Heterogeneous Self Distillation in the Wild by Yang Zhang; Huilin Pan; Yang Zhou; Mingying Li; Guodong Sun is a Engineering article available to read on EtoBox.

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Efficient visual fault detection of freight trains is a critical part of ensuring the safe operation of railways under the restricted hardware environment. Although deep learning-based approaches have excelled in object detection, the efficiency of freight train fault detection is still insufficient to apply in real-world engineering. This paper proposes a heterogeneous self-distillation framework to ensure detection accuracy and speed while satisfying low resource requirements. The privileged information in the output feature knowledge can be transferred from the teacher to the student model through distillation to boost performance. We first adopt a lightweight backbone to extract features and generate a new heterogeneous knowledge neck. Such neck models positional information and long-range dependencies among channels through parallel encoding to optimize feature extraction capabilities. Then, we utilize the general distribution to obtain more credible and accurate bounding box estimates. Finally, we employ a novel loss function that makes the network easily concentrate on values near the label to improve learning efficiency. Experiments on four fault datasets reveal that our fr

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Author

Yang Zhang; Huilin Pan; Yang Zhou; Mingying Li; Guodong Sun

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)