Grant: Applied Research and Development Grants - Level 1

Mobile apps enable companies to place maintenance/repair service requests instantly from anywhere and tracktheir progress with real-time updates. However, real-time automatic identification of equipment that isexperiencing a technical issue would be even more valuable information for both service providers and facilitymanagers.GetResQ Ltd., the industry partner for this research collaboration with Sheridan College, currently has a mobileapp that offers a variety of innovative solutions for restaurant maintenance needs. Through this proposedEngage grant, GetResQ aims to add a new service to the mobile app to identify specific equipment in a facilitybased on a digital image of the equipment. The Sheridan research team proposes to design, implement and testan algorithm to enable this service. The proposed algorithm is based on a combination of template matchingand machine learning algorithms using feature-based techniques such as Edge. For example, when a facilitymanager notices an equipment issue, he could simply take a photo of the equipment and the mobile app wouldautomatically identify the equipment and notify the appropriate technical team. The algorithm aims to targetdense environments that contain adjacent similar equipment where automatic identification of specificequipment is complex. The applied research to be undertaken during this project will add useful features toGetResQ's current mobile app, thereby attracting new customers. In addition, it will engage students inaddressing real-world problems, which will increase their potential for contributing to future innovation.