Vision-based road friction coefficient estimation received extensive attention in the field of road maintenance and autonomous driving. However, the current mainstream coarse-grained friction estimation methods are basically based on image classification tasks. This makes it difficult to deal with complex road conditions in changing weathers. Many models can correctly predict the friction coefficients of the road as a whole in consistent and simple road conditions, but perform poorly otherwise. The existing image benchmarks in this field rarely consider the above problems as well, which limits the comparable evaluations of different models. Therefore, in this paper, we first construct a challenging pixel-level friction coefficient estimation dataset WRF-P to evaluate model performances under mixed road conditions. Then, we propose a friction coefficient estimation method based on weakly supervised learning and uncertainty estimation to realize pixel-level road friction prediction with low annotation cost. The model outperforms existing weakly supervised methods and reaches 39.63% mIOU on the WRF-P dataset. The WRF-P dataset will be made publicly available at https://github.com/blackholeLFL/The-WRF-dataset soon.

Upsampling operation is necessary for semantic segmentation and other pixel-level prediction tasks. Among the commonly used upsampling operations, some are too simple to effectively recover the spatial details lost during downsampling process, and some are too complex and have high computation complexity. In real-world applications, it is critical to achieve high accuracy and maintain real-time inference speed. Therefore, an efficient upsampling operation is essential for these tasks. In this paper, we introduce efficient adaptive upsampling module (EAUM) for real-time semantic segmentation. Inspired by dynamic filter networks, EAUM adaptively predicts the kernel weight of each point in the upsampled feature map according to the corresponding points in the input feature map. To reduce computational cost, EAUM decomposes the spatial information and channel information required for upsampling. The proposed EAUM shows impressive performance on Cityscapes and CamVid benchmarks. Specifically, DenseENet with EAUM outperforms the baseline by 1.4% [Formula: see text] and 1.6% [Formula: see text] in accuracy with a slight drop in inference speed on Cityscapes test dataset.

Real-time semantic segmentation is a challenging task as both accuracy and inference speed need to be considered simultaneously. In real-world applications, it is usually achieved by deploying a deep neural network in modern GPU device. However, most of the work focused on real-time semantic segmentation is designed by significantly reducing computation complexity and model size. There are other factors that have a significant impact on inference speed are overlooked, especially when the network is running in modern GPU device. In this paper, we focus on designing a GPU-efficient network as backbone for real-time semantic segmentation. Dense connectivity can preserve and accumulate feature maps of multiple receptive fields and is therefore ideal for semantic segmentation. Therefore, we design a GPU-efficient network (DenseENet) with dense connectivity. The proposed DenseENet shows an obvious advantage in balancing accuracy and inference speed in modern GPU device. Specifically, on Cityscapes test set, DenseENet with a simple FCN decoder achieves 75.2% mIoU with 83.6 FPS for an input of 1024 × 2048 resolution and 73.6% mIoU with 132 FPS for an input of 768 × 1536 resolution on a single GTX 1080Ti card.