Abstract This paper aims to detect road regions based on a two‐scaled deep neural network. The information from different scales is helpful to boost the performance of deep learning models, and it is also a widely used strategy in various computer vision applications. In the two‐scaled model, skip‐architecture and fully convolutional layers are used to fuse the low‐level details and high‐level semantic information. It enables to detect the road areas by multi‐scale feature maps from different reception fields. To avoid the redundancy of scale information and the loss of features caused by the pooling layer, the feature maps before the first pooling layer are adopted in our model. By the convolutional kernels, our model can balance the information of two scales automatically. The loss function is also improved, in which the intersection over union (IoU) term is taken into account to guide the model to learn more features on the whole road regions. Comprehensive experiments on three benchmark datasets demonstrate that this approach can reach state‐of‐the‐art performance.

Binary grid mask representation is broadly used in instance segmentation. A representative instantiation is Mask R-CNN which predicts masks on a 28×28 binary grid. Generally, a low-resolution grid is not sufficient to capture the details, while a high-resolution grid dramatically increases the training complexity. In this paper, we propose a new mask representation by applying the discrete cosine transform(DCT) to encode the high-resolution binary grid mask into a compact vector. Our method, termed DCT-Mask, could be easily integrated into most pixel-based instance segmentation methods. Without any bells and whistles, DCT-Mask yields significant gains on different frameworks, backbones, datasets, and training schedules. It does not require any pre-processing or pre-training, and almost no harm to the running speed. Especially, for higher-quality annotations and more complex backbones, our method has a greater improvement. Moreover, we analyze the performance of our method from the perspective of the quality of mask representation. The main reason why DCT-Mask works well is that it obtains a high-quality mask representation with low complexity.