ArticleSensors (Basel, Switzerland)2024
Bearing-DETR: A Lightweight Deep Learning Model for Bearing Defect Detection Based on RT-DETR.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A lightweight metallographic defect detection framework via structured priors and energy-aware attention under strong grain boundary interference.Scientific reports · 2026Article
- A lightweight cross-scale EDS-DETR model for hazard detection in transmission corridors.Scientific reports · 2025Article
- YOLOv8n-GSS-Based Surface Defect Detection Method of Bearing Ring.Sensors (Basel, Switzerland) · 2025Article
- Balancing complexity and accuracy for defect detection on filters with an improved RT-DETR.Scientific reports · 2025Article
- A novel edge crop method and enhanced YOLOv5 for efficient wind turbine blade damage detection.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Detecting bearing defects accurately and efficiently is critical for industrial safety and efficiency. This paper introduces Bearing-DETR, a deep learning model optimised using the Real-Time Detection Transformer (RT-DETR) architecture. Enhanced with Dysample Dynamic Upsampling, Efficient Model Optimization (EMO) with Meta-Mobile Blocks (MMB), and Deformable Large Kernel Attention (D-LKA), Bearing-DETR offers significant improvements in defect detection while maintaining a lightweight framework suitable for low-resource devices. Validated on a dataset from a chemical plant, Bearing-DETR outperformed the standard RT-DETR, achieving a mean average precision (mAP) of 94.3% at IoU = 0.5 and 57.5% at IoU = 0.5-0.95. It also reduced floating-point operations (FLOPs) to 8.2 G and parameters to 3.2 M, underscoring its enhanced efficiency and reduced computational demands. These results demonstrate the potential of Bearing-DETR to transform maintenance strategies and quality control across manufacturing environments, emphasising adaptability and impact on sustainability and operational costs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.