ArticleMaterials (Basel, Switzerland)2019
Research on Recognition Technology of Aluminum Profile Surface Defects Based on Deep Learning.
Article in Materials (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Article
- Surface Defect Detection of Aluminum Profiles Based on Multiscale and Self-Attention Mechanisms.Sensors (Basel, Switzerland) · 2024Article
- Article
- Aluminum surface defect detection method based on a lightweight YOLOv4 network.Scientific reports · 2023Article
- Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys.Materials (Basel, Switzerland) · 2020Article
- Mask Gradient Response-Based Threshold Segmentation for Surface Defect Detection of Milled Aluminum Ingot.Sensors (Basel, Switzerland) · 2020Article
- SurfNetv2: An Improved Real-Time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Aluminum profile surface defects can greatly affect the performance, safety, and reliability of products. Traditional human-based visual inspection has low accuracy and is time consuming, and machine vision-based methods depend on hand-crafted features that need to be carefully designed and lack robustness. To recognize the multiple types of defects with various size on aluminum profiles, a multiscale defect-detection network based on deep learning is proposed. Then, the network is trained and evaluated using aluminum profile surface defects images. Results show 84.6%, 48.5%, 96.9%, 97.9%, 96.9%, 42.5%, 47.2%, 100%, 100%, and 43.3% average precision (AP) for the 10 defect categories, respectively, with a mean AP of 75.8%, which illustrate the effectiveness of the network in aluminum profile surface defects detection. In addition, saliency maps also show the feasibility of the proposed network.
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Registered trials
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