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Double parking has many negative effects on traffic indicators such as traffic congestion, traffic flow conditions, and traffic safety. Double parking includes parameters that affect drivers' behavioral and traffic habits. Various inspection activities and penal sanctions are implemented to prevent parking violations. Within the scope of this study, it is aimed to detect double parking with the YOLOv8 model, one of the deep learning algorithms. In this direction, a data set consisting of a total of 891 images was created, taking into account the streets with high traffic density in İzmit and Erzurum. As a result of the YOLO model, the measurement parameter F1 score value was obtained as 0.83. The mAP@0.5 values of the model for double parking, normal parking and the entire data set were obtained as 0.851, 0.922 and 0.886, respectively. When other performance parameters were examined, it was concluded that the model successfully detected the double parking situation. According to the model performance results, 89% of double and normal parking situations were detected correctly. A data set infrastructure has been created for studies on the detection of double parking. With this study, the initial work of the systems for automatic detection of parking violations and instant warning of drivers was carried out. [ABSTRACT FROM AUTHOR]