An Improved MonoDepth2 Algorithm for Vehicle Monocular Depth Estimation

Abstract

Depth estimation algorithms are of great significance for autonomous vehicle ranging. In order to solve the problems of large errors, fuzzy image generation and detail loss in depth estimation algorithms, this paper proposes an improved algorithm of the MonoDepth2 algorithm. First, the feature extraction network of the original algorithm is changed to the ResNeXt block to improve the running speed and enrich feature information. Secondly, the Convolutional Block Attention Module attention mechanism is added to the depth estimation network to improve the algorithm accuracy and promote detail detection effect. Finally, the Atrous Spatial Pyramid Pooling network structure is added between the encoder and decoder of the depth estimation network to optimize feature fusion at different scales and extract more detailed features. Compared with the original MonoDepth2 algorithm, the improved algorithm reduces the Absolute Relative Error by 0.4 %, the Squared Relative Error by 6.1 %, the Root Mean Square Error by 15 %, and the accuracy(thr=1.25), increased by 0.4 %, The generated depth map recovers deeper image details and a more detailed target contour.