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Systems able to assist drivers in the safe driving of vehicles provide several advantages, such as the reduction of traffic accidents, mostly with fatalities, normally caused by human failures, whether for distractions or even problems related to lighting or climate change. Based on this, this research aims to present a computational model capable of detect stop signs and speed limit signs, so that it contributes to the development of progressively intelligent vehicles. The system was implemented in Phyton programming language, with the support of OpenCV library, and it was divided into two steps: firstly it was performed the training and classification of the objects through Haar Cascade classification method, and in the second step, in order to improve the results, colors relevant to the object were identified using the HSV color space. During the experiments, the proposed algorithm presented satisfactory results, with a hit rate of 91% for speed limit signs and 93% for stop signs. In order to refine the proposed solution, it is intended for the next steps to include traffic sign information recognition, to either describe the specified speed on the detected objects and further reduce false positives.

Algoritmo Haar Cascade Aplicado na Detecção das Placas de Parada Obrigatória e de Velocidade Máxima Permitida