MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection

Accurate detection of pedestrian lanes is a crucial criterion for vision-impaired people to navigate freely and safely. The current deep learning methods have achieved reasonable accuracy at this task. However, they lack practicality for real-time pedestrian lane detection due to non-optimal accuracy, speed, and model size trade-off. Hence, an optimized deep neural network (DNN) for pedestrian lane detection is required. Designing a DNN from scratch is a laborious task that requires significant experience and time. This paper proposes a novel neural architecture search (NAS) algorithm, named MSD-NAS, to automate this laborious task. The proposed method designs an optimized deep network with multi-scale input branches, allowing the derived network to utilize local and global contexts for predictions. The search is also performed in a large and generic space that includes many existing hand-designed network architectures as candidates. To further boost performance, we propose a Short-term Visual Memory mechanism to improve information facilitation within the derived networks. Evaluated on the PLVP3 dataset of 10,000 images, the DNN designed by MSD-NAS achieves state-of-the-art accuracy (0.9781) and mIoU (0.9542), while being 20.16 times faster and 2.56 times smaller than the current best deep learning model.