  For vehicles equipped with the automatic parking system, the accuracy and
speed of the parking slot detection are crucial. But the high accuracy is
obtained at the price of low speed or expensive computation equipment, which
are sensitive for many car manufacturers. In this paper, we proposed a detector
using CNN(convolutional neural networks) for faster speed and smaller model
size while keeps accuracy. To achieve the optimal balance, we developed a
strategy to select the best receptive fields and prune the redundant channels
automatically after each training epoch. The proposed model is capable of
jointly detecting corners and line features of parking slots while running
efficiently in real time on average processors. The model has a frame rate of
about 30 FPS on a 2.3 GHz CPU core, yielding parking slot corner localization
error of 1.51$\pm$2.14 cm (std. err.) and slot detection accuracy of 98\%,
generally satisfying the requirements in both speed and accuracy on on-board
mobile terminals.
