A method based on context information enhancement and depth guidance was proposed to fully utilize the feature information provided by a monocular image. An efficient context information enhancement module was proposed to adaptively enhance the context information for multi-scale objects by using multiple large kernel convolutions, and the depth-wise separable convolution and strip convolution were adopted to effectively reduce the parameter count and computational complexity associated with large kernel convolutions. The prediction errors of each attribute in the 3D object bounding box were analyzed, and the primary cause of the large deviation in the prediction bounding box is the inaccurate prediction of the length and depth of the 3D object. A depth error weighted loss function was proposed to provide supervision for the predictions of length and depth for the 3D object during the training process. By using the proposed loss function, the prediction accuracy of the length and depth attributes was improved, and the accuracy of the 3D prediction bounding box was enhanced. Experiments were conducted on the KITTI dataset, and the results showed that the proposed method achieved higher accuracy than existing monocular 3D object detection methods at multiple levels of the dataset.