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Found 5 papers, 3 papers with code
Robust Semantic Segmentation in Adverse Weather Conditions by means of Fast Video-Sequence Segmentation
Computer vision tasks such as semantic segmentation perform very well in good weather conditions, but if the weather turns bad, they have problems to achieve this performance in these conditions.
Separable Convolutional LSTMs for Faster Video Segmentation
The advantage of video segmentation approaches compared to single image segmentation is that temporal image information is considered, and their performance increases due to this.
Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion
One possibility to still obtain reliable results is to observe the environment with different sensor types, such as camera and lidar, and to fuse the sensor data by means of neural networks, since different sensors behave differently in diverse weather conditions.
Semantic Segmentation of Video Sequences with Convolutional LSTMs
The disadvantage of this is that temporal image information is not considered, which improves the performance of the segmentation approach.
Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions
In this work, different sensor fusion architectures are compared for good and adverse weather conditions for finding the optimal fusion architecture for diverse weather situations.
