  In the field of autonomous driving, camera sensors are extremely prone to
soiling because they are located outside of the car and interact with
environmental sources of soiling such as rain drops, snow, dust, sand, mud and
so on. This can lead to either partial or complete vision degradation. Hence
detecting such decay in vision is very important for safety and overall to
preserve the functionality of the "autonomous" components in autonomous
driving. The contribution of this work involves: 1) Designing a Deep
Convolutional Neural Network (DCNN) based baseline network, 2) Exploiting
several network remodelling techniques such as employing static and dynamic
group convolution, channel reordering to compress the baseline architecture and
make it suitable for low power embedded systems with nearly 1 TOPS, 3)
Comparing various result metrics of all interim networks dedicated for soiling
degradation detection at tile level of size 64 x 64 on input resolution 1280 x
768. The compressed network, is called SoildNet (Sand, snOw, raIn/dIrt, oiL,
Dust/muD) that uses only 9.72% trainable parameters of the base network and
reduces the model size by more than 7 times with no loss in accuracy
