Work

TLEAR‐Net: A Network for Defect Detection in Train Wheelset Treads Based on Transfer Learning and Edge Adaptive Reinforcement Attention

Year: 2025

Type: article

Abstract: ABSTRACT As a critical load‐bearing and running component of railway systems, the wheelset's operational safety fundamentally depends on precise detection and localisation of tread defects. Current deep learning‐based detection methods face significant challenges in extracting discriminative edge features under small‐sample conditions, leading to suboptimal defect localisation accuracy. To address these limitations, this study proposes TLEAR‐Net, a novel defect detection framework integrating transfer learning with an edge‐adaptive reinforcement attention mechanism. The methodology employs RetinaNet as the baseline architecture, enhanced through multi‐stage domain adaptation using COCO 2017 pretraining and parameter‐shared ResNet‐50 backbone optimisation to bridge cross‐domain feature discrepancies. An innovative edge‐adaptive reinforcement (EAR) attention module is developed to selectively amplify defect boundary features through learnable gradient operators and hybrid spatial‐channel attention mechanisms. Comprehensive evaluations on a proprietary data set annotated defect samples demonstrate the framework's superior performance, achieving state‐of‐the‐art detection accuracy (89.22% mAP) while maintaining real‐time processing capability (42.45 FPS).

Source: IET Image Processing

DOI: 10.1049/ipr2.70060

Authors: Xinliang Hu, Jing He, Changfan Zhang, Xiang Cheng

Institutions: ChenZhou Vocational Technical College, Hunan University of Technology, Hunan Railway Professional Technology College

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,740