Detection accuracy in automatic parking is often compromised by complex environments, leading to high missed detection rates. To address these challenges, an improved multi-target detection model for parking scenes based on YOLOv5, named PSMD-YOLOv5 (parking scene multi-target detection YOLOv5), was proposed. A hybrid pooling method was used to improve the spatial pyramid pooling fast (SPPF) module, enhancing the model's ability to extract global information and improving detection performance. The bidirectional feature pyramid network (BiFPN) structure was introduced to adjust the importance of multi-scale features, strengthening feature fusion capability. A coordinate attention (CA) mechanism with large convolutional kernels was incorporated to enhance target regions of interest, further improving feature extraction. The complete intersection over union (CIoU) loss function was replaced with the minimum point distance based intersection over union (MPDIoU) loss function to accelerate model convergence and enhance localization accuracy. Experimental results show that on the automatic parking scene dataset, the PSMD-YOLOv5 model achieves precision, recall, and mAP values of 81.8%, 80.3%, and 85.3%, respectively. Compared to the original YOLOv5 model, overall precision, recall, and mAP are improved by 1%, 6.5%, and 5.2%. Furthermore, the effectiveness of the proposed model is verified through comparisons with other target detection models.