Parallel Dilated CNN for Detecting and Classifying Defects in Surface Steel Strips in Real-Time

Ahmed, Khaled R.

doi:10.1007/978-3-030-82193-7_11

Cited by 8 publications

(1 citation statement)

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“…The main objective of this research is to enhance steel strips surface defects detection accuracy and produce a significant prediction model. Therefore, in response to the above challenges, we proposed a CNN, called DSTEELNet for detecting and classifying defects in surface steel strips that aggregates different feature maps in parallel without losing resolution or analyzing rescaled images [ 28 ]. The proposed module is based on parallel stacks of different dilated convolutions that support exponential expansion of the receptive field without loss of coverage or resolution.…”

Section: Introductionmentioning

confidence: 99%

DSTEELNet: A Real-Time Parallel Dilated CNN with Atrous Spatial Pyramid Pooling for Detecting and Classifying Defects in Surface Steel Strips

Ahmed

2023

Sensors

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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% mAP in detecting defects in surface steel strips on the augmented dataset GNEU and Severstal datasets and is able to detect defects in a single image in 23ms.

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Section: Introductionmentioning

confidence: 99%