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

Abstract

Systems able to assist drivers in the safe driving of vehiclesprovide several advantages, such as the reduction of trafficaccidents, mostly with fatalities, normally caused by humanfailures, whether for distractions or even problems related tolighting or climate change. Based on this, this research aims topresent a computational model capable of detect stop signs andspeed limit signs, so that it contributes to the development ofprogressively intelligent vehicles. The system was implementedin Phyton programming language, with the support of OpenCVlibrary, and it was divided into two steps: firstly it wasperformed the training and classification of the objects throughHaar Cascade classification method, and in the second step, inorder to improve the results, colors relevant to the object wereidentified using the HSV color space. During the experiments,the proposed algorithm presented satisfactory results, with a hitrate of 91% for speed limit signs and 93% for stop signs. In orderto refine the proposed solution, it is intended for the next stepsto include traffic sign information recognition, to either describethe specified speed on the detected objects and further reducefalse positives.