Comparison of Faster RCNN and RetinaNet for Car Recognition in Adverse Weather
Subject Areas : electrical and computer engineeringYaser Jamshidi 1 , Raziyeh Sadat Okhovat 2 *
1 - Student
2 -
Keywords: Object recognition, vehicle detection, deep learning, intelligent transportation, image processing in adverse weather,
Abstract :
Vehicle detection and tracking plays an important role in self-driving cars and smart transportation systems. Adverse weather conditions, such as the heavy snow, fog, rain, dust, create dangerous limitations by reducing camera visibility and affect the performance of detection algorithms used in traffic management systems and autonomous cars. In this article, Faster RCNN deep object recognition network with ResNet50 core and RetinaNet network is used and the accuracy of these two networks for vehicle recognition in adverse weather is investigated. The used dataset is the DAWN file, which contains real-world images collected with different types of adverse weather conditions. The obtained results show that the presented method has increased the detection accuracy from 0.2% to 75% in the best case, and the highest increase in accuracy is related to rainy conditions.
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