Wafer Surface Defect Detection Based On Improved YOLOv3 Network

Cao, Xiaoke; Zhang, Fangwen; Yi, Chengdong; Tang, Kai; Bian, Ting; Yang, Mi

doi:10.1109/icmcce51767.2020.00323

Cited by 6 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kawakami et al used YOLOv2 to detect medical images, verifying the practicability of the method in terms of precision and speed [27]. Cao et al introduced the SPP module into the backbone network of YOLOv3 and the feature fusion network of YOLOv3, and used the Mish activation function to improve the problem of poor detection performance of small objects, which was caused by the lack of local information [28]. Xie et al introduced the multistage residual hybrid attention module (MRHAM) into the YOLOv4 network structure and clustered prior frames using the K-means++ clustering algorithm.…”

Section: Related Work 21 Application Of Yolo In Mask-wearing Detectionmentioning

confidence: 99%

Small target detection algorithm based on attention mechanism and data augmentation

Wang,

Liu,

et al. 2024

SIViP

View full text Add to dashboard Cite

The detection of masks is of great significance to the prevention of occupational diseases such as infectious diseases and dust diseases. For the problems of small target size, large number of targets, and mutual occlusion in mask-wearing detection, a mask-wearing detection algorithm based on improved YOLOv5s is proposed in present work. First, the ultra-lightweight attention mechanism module ECA was introduced in the Neck layer to improve the accuracy of the model. Second, the influence of different loss functions (GIoU, CIoU, and DIoU) on the improved model was explored, and CIoU was determined as the loss function of the improved model. Besides, the improved model adopted the label smoothing method at the data processing stage, which effectively improved the generalization ability of the model and reduced the risk of overfitting. Finally, the influence of data augmentation methods (Mosaic and Mixup) on model performance was discussed, and the optimal weight of data augmentation was determined. The proposed model was tested on the verification set and the mean precision (mAP), precision, and recall are 92.1%, 90.3%, and 87.4%, respectively. The mAP of the improved algorithm is 4.4% higher than that of the original algorithm.

show abstract

Section: Related Work 21 Application Of Yolo In Mask-wearing Detectionmentioning

confidence: 99%

Small target detection algorithm based on attention mechanism and data augmentation

Wang,

Liu,

et al. 2024

SIViP

View full text Add to dashboard Cite

show abstract

“…These iterations have seen a steady improvement in detection performance, garnering extensive attention and becoming a focal point of research within the industry [15,16]. Cao et al improved the YOLOv3 network model to solve the problem of difficult detection of acceptable defects on the wafer surface, and mAP increased by 13 percentage points and improved the detection speed [17]. Lan et al utilized YOLOv3 as a foundational model and introduced the integration of the batch normalization (BN) layer with the convolutional layer.…”

Section: Related Workmentioning

confidence: 99%

A YOLOv8-Based Approach for Real-Time Lithium-Ion Battery Electrode Defect Detection with High Accuracy

Zhou,

Yu,

Wang

et al. 2023

Electronics

View full text Add to dashboard Cite

Targeting the issue that the traditional target detection method has a high missing rate of minor target defects in the lithium battery electrode defect detection, this paper proposes an improved and optimized battery electrode defect detection model based on YOLOv8. Firstly, the lightweight GhostCony is used to replace the standard convolution, and the GhostC2f module is designed to replace part of the C2f, which reduces model computation and improves feature expression performance. Then, the coordinate attention (CA) module is incorporated into the neck network, amplifying the feature extraction efficiency of the improved model. Finally, the EIoU loss function is employed to swap out the initial YOLOv8 loss function, which improves the regression performance of the network. The empirical findings demonstrate that the enhanced model exhibits increments in crucial performance metrics relative to the original model: the precision rate is elevated by 2.4%, the recall rate by 2.3%, and the mean average precision (mAP) by 1.4%. The enhanced model demonstrates a marked enhancement in the frames per second (FPS) detection rate, significantly outperforming other comparative models. This evidence indicates that the enhanced model aligns well with the requirements of industrial development, demonstrating substantial practical value in industrial applications.

show abstract

An Improved Deep Learning Network Based Defect Detection Algorithm for Lithium-ion Battery Pole Chip

Zhou

et al. 2023

2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)

View full text Add to dashboard Cite

Wafer Surface Defect Detection Based On Improved YOLOv3 Network

Cited by 6 publications

References 0 publications

Small target detection algorithm based on attention mechanism and data augmentation

Small target detection algorithm based on attention mechanism and data augmentation

A YOLOv8-Based Approach for Real-Time Lithium-Ion Battery Electrode Defect Detection with High Accuracy

An Improved Deep Learning Network Based Defect Detection Algorithm for Lithium-ion Battery Pole Chip

Contact Info

Product

Resources

About