Review — SoildNet: Soiling Degradation Detection in Autonomous Driving



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Review — SoildNet: Soiling Degradation Detection in Autonomous Driving

SoildNet for Soiling Detection, Formed by Dynamic Group Convolution From ResNeXt, and Channel Reordering From ShuffleNet V1

In this story, SoildNet: Soiling Degradation Detection in Autonomous Driving, (SoildNet), by Valeo India, is reviewed.

  • Camera sensors are extremely prone to soiling such as rain drops, snow, dust, sand, mud and so on.

In this paper:

  • SoildNet (Sand, snOw, raIn/dIrt, oiL, Dust/muD) is proposed with the use of dynamic group convolution and channel reordering, make it suitable for low power embedded systems.
  • Soiling is detected at tile level of size 64×64 on 1280×768 input image.
  • Clean, opaque soiling and transparent soiling are classified.

This is a paper in 2019 NeurIPS Workshop. (Sik-Ho Tsang @ Medium)

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