ArticleSensors (Basel, Switzerland)2023
DSTEELNet: A Real-Time Parallel Dilated CNN with Atrous Spatial Pyramid Pooling for Detecting and Classifying Defects in Surface Steel Strips.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Model-Centric or Data-Centric Approach? A Case Study on the Classification of Surface Defects in Steel Hot Rolling Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2026Article
- FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning.Sensors (Basel, Switzerland) · 2023Article
- Defect Inspection Using Modified YoloV4 on a Stitched Image of a Spinning Tool.Sensors (Basel, Switzerland) · 2023Article
- Smart Pothole Detection Using Deep Learning Based on Dilated Convolution.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
1 author.
Funding
Abstract
Automatic defects inspection and classification demonstrate significant importance in improving quality in the steel industry. This paper proposed and developed DSTEELNet convolution neural network (CNN) architecture to improve detection accuracy and the required time to detect defects in surface steel strips. DSTEELNet includes three parallel stacks of convolution blocks with atrous spatial pyramid pooling. Each convolution block used a different dilation rate that expands the receptive fields, increases the feature resolutions and covers square regions of input 2D image without any holes or missing edges and without increases in computations. This work illustrates the performance of DSTEELNet with a different number of parallel stacks and a different order of dilation rates. The experimental results indicate significant improvements in accuracy and illustrate that the DSTEELNet achieves of 97%
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Registered trials
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