The development and prospect of surface defect detection based on vision measurement method

Yuan, Lunxi; Zhang, Zhengtao; Tao, Xu

doi:10.1109/wcica.2016.7578461

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…In terms of the equation, the output of the general network at the layer is (1) And for the ResNet model, the identity function from the upper layer of inputs is added to this.…”

Section: Mechanism Of Densenet Networkmentioning

confidence: 99%

“…Pulsed eddy current inspection technique [1] , which is not widely available due to its large equipment, although it has a fast detection rate, high accuracy, and can be used in high temperature states, narrow areas of the workpiece, and deep hole walls (including pipe walls). Metal magnetic memory detection does not require a special magnetization device, using its own magnetization phenomenon, can quickly and accurately determine the stress concentration area, and high sensitivity, but it is a weak magnetic signal detection method, the signal is vulnerable to material factors interference.…”

Section: Introductionmentioning

confidence: 99%

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DenseNet network-based surface defect detection algorithm for strip steel

2022

IJFET

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To address the shortcomings of current surface defect detection algorithm with many parameters, slow detection rate and low accuracy, a defect detection algorithm based on DenseNet network is proposed to mitigate the effects of gradient disappearance and gradient explosion with its more aggressive dense connection mechanism, which also reduces the number of parameters to some extent. Meanwhile, the enhancement effect of SENet network on the effective features is utilized to optimize the network model and enhance the accuracy. Using the strip steel surface defect dataset released by Northeastern University as the base defect sample, the enhancement operation is performed on it. The accuracy of the present algorithm tested on this dataset is as high as 99.44%, which is higher than that of the unimproved DenseNet network, and in terms of computational speed, the present algorithm is only 1.9ms/frame, which obtains a significant improvement compared to the DenseNet network.

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“…In terms of the equation, the output of the general network at the layer is (1) And for the ResNet model, the identity function from the upper layer of inputs is added to this.…”

Section: Mechanism Of Densenet Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%