Parking slot detection on fisheye images plays a crucial role in vehicle planning and has recently become a rapidly advancing domain in autonomous driving. Previous work struggles with practicality due to their lack of a large-scale parking slot dataset and inflexible representations of parking slots. To alleviate this, we release ParkScape, the first large-scale fisheye dataset for parking slot detection. It has 72 000 corner-based parking slots that cover abundant diversities in scenes, density, and location. Moreover, we build a fisheye parking slot detector based on a dense single-stage object detection framework. With the detector constructed, a novel dynamic feature fusion (DFF) module is proposed to mitigate the fisheye distortions by integrating context features. Our proposed DFF module can be easily plugged into existing keypoint detectors to improve performance. Extensive experiments on ParkScape validate the superiority of our fisheye parking slot detector, achieving an impressive mean average precision (AP) of 47% while operating at a remarkable speed of 54 frames per second (FPS). The dataset and code will be available at https://github.com/Vipermdl/ParkScape.