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A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU
Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …
DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism
Objective. Computer vision-based up-to-date accurate damage classification and
localization are of decisive importance for infrastructure monitoring, safety, and the …
localization are of decisive importance for infrastructure monitoring, safety, and the …
Surface defect detection and classification of steel using an efficient Swin Transformer
Detecting steel-surface defects is a crucial phase in steel manufacturing; however,
accurately completing the detection task is challenging. The Swin Transformer, a self …
accurately completing the detection task is challenging. The Swin Transformer, a self …
Survey on AI applications for product quality control and predictive maintenance in industry 4.0
Recent technological advancements such as IoT and Big Data have granted industries
extensive access to data, opening up new opportunities for integrating artificial intelligence …
extensive access to data, opening up new opportunities for integrating artificial intelligence …
Surface defect detection of aeroengine blades based on cross-layer semantic guidance
In the production process of aeroengine blades (AEBs), the surface defect detection of
blades is substantial. Currently, most blade detection methods are aimed at large blades …
blades is substantial. Currently, most blade detection methods are aimed at large blades …
STFE-Net: a multi-stage approach to enhance statistical texture feature for defect detection on metal surfaces
Statistical texture features are essential for metal surface defect detection. However, low
contrast and cluttered backgrounds exacerbate the intrinsic blurriness and variability of …
contrast and cluttered backgrounds exacerbate the intrinsic blurriness and variability of …
Efficient multi-branch dynamic fusion network for super-resolution of industrial component image
This work aims to promote the application of a high-performance super-resolution (SR)
method in industry. Considering the lack of industrial datasets to evaluate performance, an …
method in industry. Considering the lack of industrial datasets to evaluate performance, an …
GDALR: Global Dual Attention and Local Representations in transformer for surface defect detection
Automated surface detection has gradually emerged as a promising and crucial inspection
method in the industrial sector, greatly enhancing production quality and efficiency …
method in the industrial sector, greatly enhancing production quality and efficiency …
From anomaly detection to defect classification
This paper proposes a new approach to defect detection system design focused on exact
damaged areas demonstrated through visual data containing gear wheel images. The main …
damaged areas demonstrated through visual data containing gear wheel images. The main …
Automatic augmentation and segmentation system for three-dimensional point cloud of pavement potholes by fusion convolution and transformer
The regular three-dimensional (3D) detection of potholes is essential for the assessment of
pavement conditions. However, some problems associated with the segmentation of …
pavement conditions. However, some problems associated with the segmentation of …