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Visibility Loss Detection for Video Camera Using Deep Convolutional Neural Networks

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 874)


The article describes the application of various machine learning methods for the analysis of images obtained from a video camera with the purpose of detection its partial or total visibility loss. Computational experiments were performed on a data set containing more than 6800 images. Support vector machine, categorical boosting and simplified modifications of VGG, ResNet, InceptionV3 architectures of neural networks are used for image classification. A comparison of the methods quality is presented. The best results in terms of classification accuracy are obtained using ResNetm and InceptionV3m architectures. The recognition accuracy is on the average more than 96%. The processing time per reduced input frame is 8–12 ms. The obtained results confirm the applicability of the proposed approach to the detection of camera visibility loss for real tasks arising in on-board machine vision systems and video surveillance systems.


  • Image recognition
  • Convolutional neural network
  • Deep learning
  • Support vector machine
  • Boosting
  • Classification
  • Visibility loss
  • Video camera

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  • DOI: 10.1007/978-3-030-01818-4_43
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Research is carried out with the financial support of The Ministry of Education and Science of the Russian Federation within the Public contract project 2.1396.2017/4.6.

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Correspondence to Dmitry Yudin .

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Ivanov, A., Yudin, D. (2019). Visibility Loss Detection for Video Camera Using Deep Convolutional Neural Networks. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing, vol 874. Springer, Cham.

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