Multi-traffic Scene Perception Model using Different Machine Learning Classifiers (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Traffic accidents are particularly serious on a rainy day, a dark night, an overcast and/or rainynight, a foggy day, and many other times with low visibility conditions. Present vision driverassistance systems are designed to perform under good-natured weather conditions. Classificationis a methodology to identify the type of optical characteristics for vision enhancement algorithmsto make them more efficient. To improve machine vision in bad weather situations, a multi-classweather classification method is presented based on multiple weather features and supervisedlearning. First, underlying visual features are extracted from multi-traffic scene images, and thenthe feature was expressed as an eight-dimensions feature matrix. Second, five supervised learningalgorithms are used to train classifiers. The analysis shows that extracted features can accuratelydescribe the image semantics, and the classifiers have high recognition accuracy rate and adaptiveability. The proposed method provides the basis for further enhancing the detection of anteriorvehicle detection during nighttime illumination changes, as well as enhancing the driver’s field ofvision on a foggy day