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ScratchNet: Detecting the Scratches on Cellphone Screen

Part of the Communications in Computer and Information Science book series (CCIS,volume 773)

Abstract

In the process of cellphone screen manufacture, equipment failures and human errors may lead to screen scratches. Traditional manual ways check scratches by human eyes, which often costs large manpower and time, but with poor effectiveness. To address this issue, we proposes an automated scratches detection method by cascading two main modules. First, the scratch filtering module detects big scratches and localizes small scratches candidates with a serial of low-level stages. Then, the scratches classification module applies a lightweight CNN model, ScratchNet, to identify each small scratch candidate whether it is real scratch or not. To train the ScratchNet, we build a Scratches on Cellphone Screen (SCS) dataset with 50K samples in 5 categories. The experimental results on SCS testing set show that the proposed method achieves an accuracy of \(96.35\%\) on classifying small scratches, which outperforms the LeNet model and the other classifiers.

Keywords

  • Scratch detection
  • Product quality inspection
  • Cellphone screen
  • Convolutional neural networks

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Acknowledgments

This work is supported in part by the National Key Research and Development Plan (Grant No. 2016YFC0801005), the National Natural Science Foundation of China (Grant No. 61402463) and Open Foundation Project of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province in China (Grant No. 16kftk01).

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Correspondence to Shiming Ge .

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Luo, Z., Xiao, X., Ge, S., Ye, Q., Zhao, S., Jin, X. (2017). ScratchNet: Detecting the Scratches on Cellphone Screen. In: , et al. Computer Vision. CCCV 2017. Communications in Computer and Information Science, vol 773. Springer, Singapore. https://doi.org/10.1007/978-981-10-7305-2_16

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  • DOI: https://doi.org/10.1007/978-981-10-7305-2_16

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