Real-Time Lightweight Monocular Depth Estimation Using Dilated-Strip Feature Enhancement

Abstract

Monocular depth estimation is an important dense prediction task for autonomous driving, robotic perception, and unmanned aerial vehicle navigation. Although recent deep networks have achieved impressive accuracy, many of them depend on large backbones and expensive context modeling modules, making deployment on resource-constrained platforms difficult. This paper proposes a lightweight supervised monocular depth estimation network that improves feature representation while maintaining a compact model size. The proposed framework adopts an efficient encoder and applies Dilated-Strip Enhancement blocks to multi-scale encoder features. By combining dilated depthwise convolution with horizontal and vertical strip convolutions, the proposed block enlarges the receptive field and captures directional spatial structures efficiently. A multi-scale fusion decoder further integrates enhanced features from different resolutions, and auxiliary supervision is used to guide intermediate depth representations. Experiments on NYU Depth V2 and KITTI show that the proposed model achieves competitive accuracy with only 4.2M parameters. In particular, the proposed method achieves strong performance on the KITTI benchmark, suggesting that the dilated-strip design is effective for outdoor scenes with elongated and directional structures.