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Found 9 papers, 4 papers with code
Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion
Our methods not only outperform the state-of-the-art works across all metrics but also efficient in terms of cost, memory, and computation.
Task-Driven Deep Image Enhancement Network for Autonomous Driving in Bad Weather
Visual perception in autonomous driving is a crucial part of a vehicle to navigate safely and sustainably in different traffic conditions.
Imbalanced Image Classification with Complement Cross Entropy
Recently, deep learning models have achieved great success in computer vision applications, relying on large-scale class-balanced datasets.
Context-Aware Multi-Task Learning for Traffic Scene Recognition in Autonomous Vehicles
Specifically, we present a lower bound for the mutual information constraint between shared feature embedding and input that is considered to be able to extract common contextual information across tasks while preserving essential information of each task jointly.
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
In the case of traffic line detection, an essential perception module, many condition should be considered, such as number of traffic lines and computing power of the target system.
Ranked #40 on
Lane Detection
on CULane
Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform
To end this, we propose an unsupervised approach to detecting road defects, using Adversarial Image-to-Frequency Transform (AIFT).
Unconstrained Road Marking Recognition with Generative Adversarial Networks
Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning.
Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution
Although most current license plate (LP) recognition applications have been significantly advanced, they are still limited to ideal environments where training data are carefully annotated with constrained scenes.
Ranked #3 on
License Plate Recognition
on AOLP-RP
SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition
In this paper, we present an algorithm for real-world license plate recognition (LPR) from a low-quality image.
Ranked #1 on
License Plate Recognition
on AOLP-RP
