High-Precision Detection Algorithm for Metal Workpiece Defects Based on Deep Learning

Xu, Xiujin; Zhang, Gengming; Zheng, Wenbin; Zhao, Anbang; Zhong, Yanfei; Wang, Hongjun

doi:10.3390/machines11080834

Cited by 5 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In table 4, the performance of YOLOv8 detectors constructed with five different attention mechanisms was tested on a self-made industrial bearing surface defect dataset (to ensure fairness, the same hyperparameters were used). The tested attention mechanisms include Polarized Self-Attention [22], CoordAttention [23], CoTAttention [24], SimAM [25], and GAM.…”

Section: Verification Analysis 351 Comparison Analysis Of Attention M...mentioning

confidence: 99%

A bearing surface defect detection method based on multi-attention mechanism Yolov8

Ding,

Zhan,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Surface defects in bearings not only affect the appearance but also impact the service life and performance. Therefore, it is imperative for bearing manufacturers to conduct quality inspections before bearings leave the factory. However, traditional visual inspection methods exhibit shortcomings such as high omission rates, insufficient feature fusion and oversized models when dealing with multiple target defects in bearings. To address these challenges, this paper proposes a surface defect detection method for bearings based on an improved Yolov8 algorithm (G-Yolov8). Firstly, a C3Ghost convolutional module based on the Ghost module is constructed in YOLOv8 to simplify model computational costs. Secondly, a Global Attention Mechanism (GAM) module is designed at the end of the backbone network to increase sensitivity to implicit small target area features and optimize feature extraction efficiency.Subsequently, a deep deformable convolution feature pyramid network (TDSFPN) is constructed by introducing the deformable convolutional networks version 2 (DCNv2) and the lightweight Content-Aware Reassembly of Features upsampling operator (CARAFE) to reduce sampling information loss and improve the fusion of multi-scale target defects. Finally, different attention mechanisms are embedded in the detection network to construct a Multi-Attention Detection Head (MADH) to replace the decoupled head, refining classification and localization tasks, reducing feature confusion, and improving the model's detection accuracy. Experimental results demonstrate that the improved algorithm achieves a 3.5% increase in mean average precision on a self-made small-scale train bearing surface defect dataset, with a 17.3% reduction in model size. This improvement not only enhances accuracy but also addresses the requirement for lightweight deployment in subsequent stages.

show abstract

Section: Verification Analysis 351 Comparison Analysis Of Attention M...mentioning

confidence: 99%

A bearing surface defect detection method based on multi-attention mechanism Yolov8

Ding,

Zhan,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…where Su denotes the intersecting part area and is calculated as Among the loss functions of WIoU, WIoUv3 [26] has better performance, and is illustrated as…”

Section: Wiou Lossmentioning

confidence: 99%

An Enhanced Single-Stage Neural Network for Object Detection in Transmission Line Inspection

Cai,

Nie,

Tong

et al. 2024

Electronics

View full text Add to dashboard Cite

To address the issue of human object detection in transmission line inspection, an enhanced single-stage neural network is proposed, which is based on the improvement of the YOLOv7-tiny model. Firstly, a lighter GSConv module is utilized to optimize the original ELAN module, reducing the parameters in the network. In order to make the network less sensitive to the targets with an unconventional pose, a module based on CSPNeXt and GSConv is designed and integrated with the ELAN module to extract deep features from the targets. Moreover, a WIoU (Wise Intersection over Union) loss function is utilized to enhance the ability of the YOLOv7-tiny model to detect objects with an unconventional pose and the interference of the background. Finally, the experimental results on human targets in transmission line inspection demonstrate that the proposed network improves detection confidence and reduces missed detection. Compared to the YOLOv7-tiny model, the proposed method promotes the performance of accuracy while reducing the amount of parameters.

show abstract

Maritime Small Object Detection Algorithm in Drone Aerial Images Based on Improved YOLOv8

Ling,

Zhang,

2024

IEEE Access

View full text Add to dashboard Cite

High-Precision Detection Algorithm for Metal Workpiece Defects Based on Deep Learning

Cited by 5 publications

References 51 publications

A bearing surface defect detection method based on multi-attention mechanism Yolov8

A bearing surface defect detection method based on multi-attention mechanism Yolov8

An Enhanced Single-Stage Neural Network for Object Detection in Transmission Line Inspection

Maritime Small Object Detection Algorithm in Drone Aerial Images Based on Improved YOLOv8

Contact Info

Product

Resources

About