Abstract
This paper proposes a detection method for parking areas and collision risk areas in parking situations. Deep learning algorithms used for area detection rely on semantic segmentation, a method of classifying pixels into semantic segmentation. The main architecture of this paper is based on a harmonic densely connected network and a cross-stage partial network. The dataset was calibrated for four 190-degree wide-angle cameras to generate 500 AVM images based on the Chungbuk National University parking lot, and the experiment was performed with this dataset. In the experimental results, we found that the available parking area can be visualized by detecting the parking line, the parking area, and the available driving area in the AVM image. We visualized the area that remained undetected, as a collision risk area, through semantic segmentation to derive the results. According to the proposed CSPHarDNet model, the experimental results obtained were 81.89% mIoU and 18.36 FPS in the NVIDIA Xavier environment.
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