  It is important, but challenging, for the forest industry to accurately map
roads which are used for timber transport by trucks. In this work, we propose a
Dense Dilated Convolutions Merging Network (DDCM-Net) to detect these roads in
lidar images. The DDCM-Net can effectively recognize multi-scale and complex
shaped roads with similar texture and colors, and also is shown to have
superior performance over existing methods. To further improve its ability to
accurately infer categories of roads, we propose the use of a joint-task
learning strategy that utilizes two auxiliary output branches, i.e, multi-class
classification and binary segmentation, joined with the main output of
full-class segmentation. This pushes the network towards learning more robust
representations that are expected to boost the ultimate performance of the main
task. In addition, we introduce an iterative-random-weighting method to
automatically weigh the joint losses for auxiliary tasks. This can avoid the
difficult and expensive process of tuning the weights of each task's loss by
hand. The experiments demonstrate that our proposed joint-task DDCM-Net can
achieve better performance with fewer parameters and higher computational
efficiency than previous state-of-the-art approaches.
