Unsupervised Monocular Depth Estimation with Edge Enhancement for Dynamic Scenes

In the dynamic scene of autonomous vehicles, the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation. To solve this problem, we propose an unsupervised monocular depth estimation model based on edge enhancement, which is specifically aimed at the depth perception challenge in dynamic scenes. The model consists of two core networks: a deep prediction network and a motion estimation network, both of which adopt an encoder-decoder architecture. The depth prediction network is based on the U-Net structure of ResNet18, which is responsible for generating the depth map of the scene. The motion estimation network is based on the U-Net structure of Flow-Net, focusing on the motion estimation of dynamic targets. In the decoding stage of the motion estimation network, we innovatively introduce an edge-enhanced decoder, which integrates a convolutional block attention module (CBAM) in the decoding process to enhance the recognition ability of the edge features of moving objects. In addition, we also designed a strip convolution module to improve the model’s capture efficiency of discrete moving targets. To further improve the performance of the model, we propose a novel edge regularization method based on the Laplace operator, which effectively accelerates the convergence process of the model. Experimental results on the KITTI and Cityscapes datasets show that compared with the current advanced dynamic unsupervised monocular model, the proposed model has a significant improvement in depth estimation accuracy and convergence speed. Specifically, the root mean square error (RMSE) is reduced by 4.8% compared with the DepthMotion algorithm, while the training convergence speed is increased by 36%, which shows the superior performance of the model in the depth estimation task in dynamic scenes.