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To meet the safety requirements of pedestrians and other vehicles in smart parking systems, vehicles rely heavily on visual data to classify and detect target objects. In real-time, deep learning (DL)-based algorithms have shown excellent results in object detection. While several studies have thoroughly investigated various DL-based object detection methods, only a few have focused on multi-detection, encompassing pedestrian detection, vehicle detection, and traffic sign detection. This work provided a comparative analysis of six independent variants of Faster-RCNN and SSD Models for pedestrians, vehicles, and traffic sign detection. There is a lack of comparison leveraging on detailed evaluation metrics among existing models. To address the issue of little or non-availability of datasets for multi-detection, this work provided a mini dataset for task (TraPedesVeh). Experimental results analyzed using detection …