关键词: 深度学习；车位检测方法；高斯分布

Abstract: In the process of manually annotating parking slot detection datasets, every effort is made to accurately mark the corner points of parking slot. However, in actual annotation images, some corner points may be blurry or obstructed by obstacles, making precise annotation difficult. This poses challenges for the model when regressing the coordinates of these corner points using the dataset. This article proposes a novel loss function for parking slot corner point offset, which simultaneously learns the corner point offset and localization variance, thereby improving the accuracy of parking space corner point localization with almost no additional computational load. On a self-constructed dataset, the proposed approach achieves higher precision, while significantly reducing both the parking slot corner localization error and the parking slot boundary angle error. Moreover, the standard deviations of these two errors are notably smaller, indicating that the model produces more stable and consistent predictions.

Key words: deep learning; parking slot detection method; Gaussian distribution