Visual Inspection of Collective Protection Equipment Conditions with Mobile Deep Learning Models

Bruno Ferreira, Bruno Lima, Tiago Vieira


Even though Deep Learning models are presenting increasing popularity in a variety of scenarios, there are many demands to which they can be specifically tuned to. We present a real-time, embedded system capable of performing the visual inspection of Collective Protection Equipment conditions such as fire extinguishers (presence of rust or disconnected hose), emergency lamp (disconnected energy cable) and horizontal and vertical signalization, among others. This demand was raised by a glass-manufacturing company which provides devices for optical-fiber solutions. To tackle this specific necessity, we collected and annotated a database with hundreds of in-factory images and assessed three different Deep Learning models aiming at evaluating the trade-off between performance and processing time. A real-world application was developed with potential to reduce time and costs of periodic inspections of the company’s security installations.


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