Available parking slot detection is the first step for autonomous parking systems. In this paper, we propose a novel parking slot detection method that uses the center information regression and the occupancy classification of the parking slot. We design a self-calibrated convolutions network (SCCN) to obtain the position, length, occupancy and direction, which can also infer the parking slot type according to the prediction results. The method divides an around view monitor (AVM) image into the 16 × 16 grid cells and performs a SCCN detector for feature extraction. Subsequently, the whole parking slot can be easily inferred via prior geometric information and detection results. We adopt the heatmap, MultiBins, and midline to detect the center keypoint, direction and occupancy, respectively. And we quantitatively evaluate the performance of the proposed method on the public PS2.0 datasets. The experimental results show the outperformance by a precision rate of 99.35%, a recall rate of 99.17%, an occupancy classification accuracy of 99.12% and all correctly inferred types of parking slots on the datasets.