To solve the problems of lack of depth information and poor detection accuracy in conventional monocular three-dimensional (3D) target detection algorithms, an algorithm for multiscale monocular 3D target detection incorporating instance depth is proposed. First, to enhance the processing ability of the model for targets with different scales, a multiscale sensing module based on hole convolution is designed. Then, the depth features containing multiscale information are refined from both spatial and channel directions to remove the inconsistencies among different scale feature maps. Further, the instance depth information is used as an auxiliary learning task to enhance the spatial depth characteristics of 3D objects, and the sparse instance depth is used to monitor the auxiliary task, thereby improving the model's 3D perception. Finally, the proposed algorithm is tested and validated on the KITTI dataset. The experimental results show that the average accuracy of the proposed algorithm in the vehicle category is 5.27% higher than that of the baseline algorithm, indicating that the proposed algorithm effectively improves the detection performance compared with the conventional monocular 3D target detection algorithms.