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Self-supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness Using On-board Videos
Self-supervised depth estimation has drawn much attention in recent years as it does not require labeled data but image sequences.
DMRVisNet: Deep Multi-head Regression Network for Pixel-wise Visibility Estimation Under Foggy Weather
Therefore, precisely estimating the visibility under foggy weather can significantly benefit traffic management and safety.
Joint Learning of Frequency and Spatial Domains for Dense Predictions
Current artificial neural networks mainly conduct the learning process in the spatial domain but neglect the frequency domain learning.
Self-supervised Multi-task Learning Framework for Safety and Health-Oriented Connected Driving Environment Perception using Onboard Camera
Extensive experiments on the synthesized data from the KITTI and real-world data collected in Beijing demonstrate that the proposed method can (1) achieve performance competitive in depth estimation as compared with state-of-the-art methods when taking clear images as input; (2) predict vivid depth map for images contaminated by various levels of haze; and (3) accurately estimate visibility, airlight, and PM2. 5 mass concentrations.
