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In the past decade, deep learning (DL) has taken the world by storm. It has produced significant results in a wide variety of applications ranging from self driving cars to natural language processing (NLP). Modern deep learning is built from a number of different algorithms including artificial neural networks (ANN), optimisation algorithms, back-propagation (BP), and varying levels of supervision. Recent advances in GPU hardware, improved availability of large, high quality datasets, and the development of modern training algorithms have all played a pivotal role in the emergence of modern d...

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 M...

Pedestrian lane detection is a crucial task in assistive navigation for vision-impaired people. It can provide information on walkable regions, help blind people stay on the pedestrian lane, and assist with obstacle detection. An accurate and real-time lane detection algorithm can improve travel safety and efficiency for the visually impaired. Despite its importance, pedestrian lane detection in unstructured scenes for assistive navigation has not attracted sufficient attention in the research community. This paper aims to provide a comprehensive review and an experimental evaluation of method...

Depth estimation is an essential component in computer vision systems for achieving 3D scene understanding. Efficient and accurate depth map estimation has numerous applications including self-driving vehicles and virtual reality tools. This paper presents a new deep network, called D-Net, for depth estimation from a single RGB image. The proposed network can be trained end-to-end, and its structure can be customised to meet different requirements in model size, speed, and prediction accuracy. Our approach gathers strong global and local contextual features at multiple resolutions, and then tr...

A new robotic cane featuring inertial sensors has been developed with the potential to help navigate people with visual impairments around indoor environments.