An Insight into Real Time Vehicle Detection and Classification Methods using ML/DL based Approach

Vehicle detection and classification is one of the major challenges for automated traffic surveillance as well as for military defence systems to identify enemy vehicles. For efficient real-time surveillance, accurate detection and classification of many vehicle kinds, including cars, trucks, buses, and so forth are important. This paper focuses on different methods of machine learning used toidentify thevehicle and then then categorize each vehicle according to the classes. It also explores the critical insight of existing vehicle detection and classification approaches using various machine learning methods like YOLO, CNN, R-CNN, and AdaBoost. The majority of the research projects now in existence solely consider increasing image-based accuracy, which has poor real-time performance and uses more computational resources.