In this study, the convolutional neural network is used to segment the scene and detect the parking space in the parking lot of the expressway service area. Firstly, the study expands the parking lot dataset of the expressway service area, and uses the convolutional neural network to segmentation of the highway parking lot and vehicle detection, the weighted neural network is used to share the weights of the feature extraction network to achieve the joint training and lightweight of the network model. Furthermore, the convolutional neural network enhances the recognition of small targets by extracting texture features of the vehicle and using pyramid feature fusion. Finally, the system uses the prior knowledge of the parking space in the expressway service area to calculate the parking space quantity information of the parking lot in real time. The practical application shows that the method has a accuracy of 94% for parking space detection and a detection speed of 25 frames per second in complex scenes. It has strong generalization ability and is suitable for parking lot detection.