Abstract
Today, various solutions are offered for traffic density. One of these suggestions is to popularize the use of bicycles in the category of non-motorized vehicles. For this, first of all, bicycle paths must be built. The use of bicycle lanes or the rate of bicycle use in normal traffic is an important data. Deep learning techniques, which have been popular in recent years, can be used to obtain this data. The aim of this study is to present a model that detects bicycles using various convolutional neural networks architectures. First of all, 962 open source bicycle images obtained from the internet are labeled. For this, trainings were conducted with YOLOv3, YOLOF, Faster R-CNN and Sparse R-CNN architectures. As a result of the trainings, a value of 0.92 mAP was reached with Faster R-CNN. At the end of the study, a software that detects bicycles in real time has been developed.
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