Architectural exploration of multilayer perceptron models for on-chip and real-time road sign classification

Abstract

Road sign recognition is part of the automatic driver assistance systems implemented on the dashboard of vehicles. The recognition task is often carried out based on a classification procedure manipulating the detected signs. Classification tasks can be resolved by the use of multilayer artificial neural network systems. This article proposes an optimized real-time on-chip hardware implementation of multilayer perceptron system used for road sign classification. Four architectural approaches were described: on the one hand, the classic and the serial optimized architectures that offer a very significant reduction in hardware resources, and, on the other hand, the parallel and the optimized architectures, which offer a much reduced, time execution. In order to benefit from the advantages of the allocated hardware resources and the classification of the runtime process, these four architectures have been implemented on field programmable gate array Virtex-6 devices and their performances were quantified and evaluated according to a cost criterion. The energy dissipated by each of these architectures was measured; the achieved results have allowed us to conclude that the serial optimized architecture is the optimal solution, since it creates a tradeoff between the low cost, and the energy efficiency, and still real-time for the considered application.

Road Sign Recognition Technology Integrating Deep Learning

“…Not only is it an important part of driver assistance systems, it can also serve as one of many inputs to environment perception systems. Efficient and accurate traffic sign detection and recognition will certainly be of great help to this research [1][2]. In the road navigation function of electronic maps, the detection and recognition of road traffic signals will also provide very important information for the driver's positioning and route planning [3][4].…”