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Intelligent vehicle detection (IVD) plays a prominent role in evolving an intelligent traffic management system (ITMS). It can help to decrease the average waiting time at the traffic post, save fuel consumption, control traffic congestion, decrease accident rates, and build up human safety. Recent developments in the artificial intelligence (AI) domain have increased the demand for IVD in the undisciplined traffic environment, which is a usual condition in developing countries. IVD is a difficult task in an undisciplined traffic environment because different vehicle categories travel very close to each other on the roads and do not follow traffic rules. Previously, several convolutional neural network (CNN) based deep learning (DL), and visual transformer-based techniques for vehicle and object detection have been presented. They are complex and do not accurately extract multi-scale features due to the involvement of …