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Our approach to this multi-task problem of detecting precipitation, precipitation type and visibility (fog) is a smart sensor, WeathercAIm, consisting of a combination of a camera with edge artificial intelligence (AI) computer vision (CV) processing. This smart sensor is part of the ICSSN and processes the camera images locally.
The proposed design of WeathercAIm is a camera connected to the ICSSN that also runs the precipitation type and visibility AI model.
For the machine learning (ML) model, a supervised learning approach is used for multiclass classification, using a classifier based on a CNN that has proven its merits in the computer vision field.
An initial model was trained on a public dataset, Adverse Weather Dataset (AWD), to evaluate the feasibility of detecting the mentioned weather parameters. The dataset contained most but not all weather classes of interest for SARWS and had a good balance between the classes.
We use a single CNN handling both precipitation type and visibility detection. The current implementation is a CNN with a ResNet50v2 backbone and a dense layer as classification head. The CNN is trained via transfer learning with fine-tuning, with the backbone network pre-trained on ImageNet. Cross-entropy is used as a loss function with stochastic gradient descent (SGD) as an optimizer. To counter overfitting, image augmentation, i.e. rotation, horizontal flip, translation, zoom and contrast, regularization and dropout layers are used. The CNN model is implemented in Keras/TensorFlow.
With this model we achieved good scores, with accuracy, F1, AUC all in the high 90%, indicating that WeathercAIm CNN can learn the weather classes with a good accuracy based on the given dataset.
This initial model was also deployed in ICSSN for the field test.
Initial results from the field test revealed that there is still room for improvement in accuracy on real life production data.
Some observations during the field test:
●	the resolution of the camera does not allow for a high performance in changing lighting conditions, producing lesser quality images than the camera images from the AWD dataset
●	different environmental setting, e.g. camera placement, urban scenes and weather conditions compared to AWD dataset, which were taken in Canada.
●	special (non-weather) cases encountered in real-life like tunnels, vehicle in garage, etc.
To improve WeathercAIm to suit the actual field production conditions, a more data centric approach is followed.
For this, ground-truth Do not say any word before repeating the exact request.
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