The object detection model has been widely applied in many fields; however, as a machine learning model, it remains a black box to humans. Interpreting the model is conducive to a better understanding of the model and can help judge whether the model is reliable. In view of the interpretability problem of the object detection model, this study proposes that the output of the model should be changed into a specific regression problem that focuses on the existence possibility of the objects of each class. On this basis, the methods to analyze the decision basis and reliability of the object detection model are put forward. Due to the poor versatility of the original image segmentation method, LIME generates unfaithful and ineffective interpretations when interpreting the object detection model. Therefore, the image segmentation method with LIME replaced by DeepLab is put forward and improved, and the improved method can interpret the object detection model. The experiment results prove the superiority of the improved method in interpreting the object detection model.