I Can See Clearly Now: Image Restoration via De-Raining

Porav, Horia; Bruls, Tom; Newman, Paul

doi:10.1109/icra.2019.8793486

Cited by 83 publications

(79 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is equipped with a 2.4 GHz 10-core Xeon CPU and Geforce Titan X Graphics Processing Unit (GPU). These permit both online and offline Deep Learning algorithms to be implemented on-board [13,14]…”

Section: Sensor and Computer Payloadmentioning

confidence: 99%

The Hulk: Design and Development of a Weather-Proof Vehicle for Long-Term Autonomy in Outdoor Environments

Kyberd

Attias

Get

et al. 2021

Field and Service Robotics

Self Cite

View full text Add to dashboard Cite

In recent years the range of robotics platforms available for research and development has increased dramatically. Despite this there are areas and applications which are not currently well served by the existing available platforms. Many of them are designed for indoor use; the range of outdoor and off-road robotics platforms is less diverse and few address the issue of deployment in hazardous weather conditions and integrate a suitable sensor suite. In addition almost all of the commercially available Unmanned Ground Vehicles (UGVs) are unsuitable for deployment on delicate surfaces. Given the widespread use of manicured grass within the built environment and agriculture across the western world, this severely limits where they can be deployed and the tasks that can be accomplished. This paper introduces the design principles of a suitable autonomous vehicle. Hulk is built from a commercial zero-turn mower modified for fly-by-wire operation and equipped with a full sensor suite and computing payload. To enable remote, long-term autonomy in diverse environments, several layers of redundant safety systems were designed and installed and the entire assembly made weatherproof.

show abstract

Section: Sensor and Computer Payloadmentioning

confidence: 99%

The Hulk: Design and Development of a Weather-Proof Vehicle for Long-Term Autonomy in Outdoor Environments

Kyberd

Attias

Get

et al. 2021

Field and Service Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…This domain adaptation method allows to compare data with drastic visual changes, hence as in [24], we need priors about the nature of the changes that will occur. Using a similar approach, authors of [61] train a neural network to remove image noise induced by rain drops over camera lens.…”

Section: Localization In Challenging Conditionmentioning

confidence: 99%

Improving Image Description with Auxiliary Modality for Visual Localization in Challenging Conditions

et al. 2020

View full text Add to dashboard Cite

Image indexing for lifelong localization is a key component for a large panel of applications, including robot navigation, autonomous driving or cultural heritage valorization. The principal difficulty in long-term localization arises from the dynamic changes that affect outdoor environments. In this work, we propose a new approach for outdoor large scale imagebased localization that can deal with challenging scenarios like cross-season, cross-weather and day/night localization. The key component of our method is a new learned global image descriptor, that can effectively benefit from scene geometry information during training. At test time, our system is capable of inferring the depth map related to the query image and use it to increase localization accuracy.We show through extensive evaluation that our method can improve localization performances, especially in challenging scenarios when the visual appearance of the scene has changed. Our method is able to leverage both visual and geometric clues from monocular images to create discriminative descriptors for cross-season localization and effective matching of images acquired at

show abstract

“…32,33 Similar efforts were also made to address robust foggy scene parsing 34,35 and rainy scene semantic segmentation. 36,37 Unsupervised learning has also been frequently leveraged to pre-process input images, in order to prevent performance from degrading catastrophically when the input domain differs significantly from previously seen domains. 15,38 Specifically, this research line is also highly related to topological localization, 38,39 where modern visual localizers like 40,41 can also benefit from the input adaptation to perform more reliably against variation challenges.…”

Section: Model Adaptionmentioning

confidence: 99%

See clearer at night: towards robust nighttime semantic segmentation through day-night image conversion

Sun

Wang

Yang

et al. 2019

Artificial Intelligence and Machine Learning in Defense Applications

View full text Add to dashboard Cite

In recent years, intelligent driving navigation and security monitoring have made considerable progress with the help of deep Convolutional Neural Networks (CNNs). As one of the state-of-the-art perception approaches, semantic segmentation unifies distinct detection tasks widely desired by both autonomous driving and security monitoring. Currently, semantic segmentation shows remarkable efficiency and reliability in standard scenarios such as daytime scenes with favorable illumination conditions. However, in face of adverse conditions such as the nighttime, semantic segmentation loses its accuracy significantly. One of the main causes of the problem is the lack of sufficient annotated segmentation datasets of nighttime scenes. In this paper, we propose a framework to alleviate the accuracy decline when semantic segmentation is taken to adverse conditions by using Generative Adversarial Networks (GANs). To bridge the daytime and nighttime image domains, we made key observation that compared to datasets in adverse conditions, there are considerable amount of segmentation datasets in standard conditions such as BDD and our collected ZJU datasets. Our GAN-based nighttime semantic segmentation framework includes two methods. In the first method, GANs were used to translate nighttime images to the daytime, thus semantic segmentation can be performed using robust models already trained on daytime datasets. In another method, we use GANs to translate different ratio of daytime images in the dataset to the nighttime but still with their labels. In this sense, synthetic nighttime segmentation datasets can be generated to yield models prepared to operate at nighttime conditions robustly. In our experiment, the later method significantly boosts the performance at the nighttime evidenced by quantitative results using Intersection over Union (IoU) and Pixel Accuracy (Acc). We show that the performance varies with respect to the proportion of synthetic nighttime images in the dataset, where the sweet spot corresponds to most robust performance across the day and night. The proposed framework not only makes contribution to the optimization of visual perception in intelligent vehicles, but also can be applied to diverse navigational assistance systems.

show abstract

I Can See Clearly Now: Image Restoration via De-Raining

Cited by 83 publications

References 35 publications

The Hulk: Design and Development of a Weather-Proof Vehicle for Long-Term Autonomy in Outdoor Environments

The Hulk: Design and Development of a Weather-Proof Vehicle for Long-Term Autonomy in Outdoor Environments

Improving Image Description with Auxiliary Modality for Visual Localization in Challenging Conditions

See clearer at night: towards robust nighttime semantic segmentation through day-night image conversion

Contact Info

Product

Resources

About