Neural-Network-Optimized Vehicle Classification Using Clustered Image and Fiber-Sensor Datasets

Internet of Things (IoT) becomes indispensable for transport and automotive industry to advance functions in on-road traffic monitoring. Indeed, smart management tools and machine learning concepts are inevitable in vehicle categorization systems. However, to date, existing systems for vehicle classification are exclusively based on singular technological platforms only. This not only limits their long-term use and future scaling, but also sets restrictions to obtain high classification accuracies with modern machine learning tools feed with diversified big volume data. In this work, we design a novel convolutional neural network (CNN) that substantially improves the on-road vehicle classification. In particular, we experimentally harness, to the best of our knowledge for the first time, two different datasets from separated technological platforms based on close-circuit television (CCTV) and fiber Bragg grating (FBG) sensors, respectively. The hybrid CNN classification system, with individual CCTV and FBG datasets, substantially improves detection levels, reaching in-class accuracy up to 90% - 97%. Moreover, this classification concept includes an intrinsic back-up verification with respect to each platform compensating the shortcomings of individual technologies. Our demonstration can make key advances towards near-unity accuracy in vehicle classifications for IoT systems, capitalizing on cost-effective and well-established platforms.