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


1. Soiling Types, Classes and Dataset

Different types of soiling: (a) grass, (b) fog, (c) rain drops, (d) dirt, (e) splashes of mud, (f) splashes of mud in night

In this study, an input image of resolution 1280×768 is annotated per tile of size 64×64 and each tile represents a soiling class, hence it is possible to see an input image that contains all soiling categories across tiles.

Camera view-wise presence of all three classes

2. SoildNet: Network Architecture

Proposed networks, Net-1 (Leftmost), Net-2, Net-3, Net-4, and SoildNet (rightmost). Conv: convolution, G: group size, S: stride size
(a) Common BGR input data layer, (b) Optimized YUV4:2:0 data layer. (Figure from YUVMultiNet)
Left: ResNet Block, Right: ResNeXt with 32 Group Convolutions (Figure from ResNeXt)
Channel Reordering/Shuffle, Performed for 3 Group Convolutions (Figure from ShuffleNet V1)
Analysis of computation complexity of all network proposals including ResNet-10

3. Embedded Platform Constraints

4. Experimental Results

Comparison of class-wise accuracy for tile level soiling degradation detection
Examples of 64×64 tile based soiling degradation detection, Color codes: Green — Clean, Cyan — Opaque, Blue — Transparent.



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