Performance Analysis of Traffic Sign Detection using Deep Learning

Traffic sign detection is a crucial element in self sustaining driving and advanced driving force assistance systems (ADAS), permitting vehicles to come across and respond to street signs. This paper provides a comparative examine of convolutional neural network (CNN) architectures, such as MASK R-CNN, rapid R-CNN, and faster R-CNN, in traffic signal detection the use of the German visitors sign popularity Benchmark (GTSRB) dataset. every version’s overall performance became evaluated in terms of detection accuracy, processing pace, and robustness beneath various conditions. The results reveal that quicker R-CNN achieves a positive exchange-off among pace and accuracy, with mask R-CNN offering the best detection accuracy but with slightly higher computational necessities. This observation gives insights into the suitability of various detection fashions for visitors signal recognition structures