Detecting Teeth Defects on Automotive Gears Using Deep Learning

Gears are a vital component in many complex mechanical systems. In automotive systems, and in particular vehicle transmissions, we rely on them to function properly on different types of challenging environments and conditions. However, when a gear is manufactured with a defect, the gear’s integrity can become compromised and lead to catastrophic failure. The current inspection process used by an automotive gear manufacturer in Guelph, Ontario, requires human operators to visually inspect all gear produced. Yet, due to the quantity of gears manufactured, the diverse array of defects that can arise, the time requirements for inspection, and the reliance on the operator’s inspection ability, the system suffers from poor scalability, and defects can be missed during inspection. In this work, we propose a machine vision system for automating the inspection process for gears with damaged teeth defects. The implemented inspection system uses a faster R-CNN network to identify the defects, and combines domain knowledge to reduce the manual inspection of non-defective gears by 66%.

Using Personas with Visual Impairments to Explore the Design of an Accessible Self-Driving Vehicle Human-Machine Interface

Recent reports have suggested that most self-driving vehicle technology being developed is not currently accessible to users with disabilities. We purport that this problem may be at least partially attributable to knowledge gaps in practice-oriented user-centered design research. Missing, we argue, are studies that demonstrate the practical application of user-centered design methodologies in capturing the needs of users with disabilities in the design of automotive systems specifically. We have investigated user-centered design, specifically the use of personas, as a methodological tool to inform the design of a self-driving vehicle human-machine interface for blind and low vision users. We then explore the use of these derived personas in a series of participatory design sessions involving visually impaired co-designers. Our findings suggest that a robust, multi-method UCD process culminating with persona development may be effective in capturing the conceptual model of persons with disabilities and informing the design of automotive system.