Pub Date : 2022-03-12DOI: 10.1109/OJCAS.2022.3174632
Laura Falaschetti;Lorenzo Manoni;Claudio Turchetti
Real-time semantic segmentation on embedded devices has recently enjoyed significant gain in popularity, due to the increasing interest in smart vehicles and smart robots. In particular, with the emergence of autonomous driving, low latency and computation-intensive operations lead to new challenges for vehicles and robots, such as excessive computing power and energy consumption. The aim of this paper is to address semantic segmentation, one of the most critical tasks for the perception of the environment, and its implementation in a low power core, by preserving the required performance of accuracy and low complexity. To reach this goal a low-rank convolutional neural network (CNN) architecture for real-time semantic segmentation is proposed. The main contributions of this paper are: i)