Real-time onboard visual parking space detection: a performance study

In this work, we present a performance study of our preliminary Automatic Parking Space Detection (APSD) system. The purpose of the APSD prototype is to enrich an information system with automatically located parking spaces. It uses images captured from a vehicle to suggest available parking spaces in urban environments. To carry out this performance evaluation, we tested three different platforms; a desktop computer with a NVIDIA RTX 2070 GPU as an upper bound performance system and two embedded solutions, a NVIDIA Jetson Xavier NX module, and a NVIDIA Jetson TX1 module. We analyze the effect of different modifications on the system, including the use of different state-of-the-art networks on the different architectures and an ablation study to verify the effect of using lower resolution images and optimizing the detection network by means of TensorRT. The evaluation results presented demonstrate the effectiveness of the proposed APSD system to meet the requirement of real-time processing. This study highlights the importance of the choice of neural network architectures used in the system, as well as the limitations of hardware devices used in the evaluation.