Maritime Environment Perception Based on Deep Learning

2022 ◽  
pp. 1-11
Author(s):  
Jiaying Lin ◽  
Phillip Diekmann ◽  
Christian-Eike Framing ◽  
Rene Zweigel ◽  
Dirk Abel
Keyword(s):  
Deep Learning ◽  
Environment Perception ◽  
Maritime Environment

scholarly journals Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shize Huang ◽  
Xiaowen Liu ◽  
Xiaolu Yang ◽  
Zhaoxin Zhang ◽  
Lingyu Yang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Gradient Descent ◽  
Learning Networks ◽  
Adversarial Examples ◽  
Environment Perception ◽  
Projected Gradient Descent ◽  
Adversarial Example ◽  
Improved Methods ◽  
Growing Neural Networks

Trams have increasingly deployed object detectors to perceive running conditions, and deep learning networks have been widely adopted by those detectors. Growing neural networks have incurred severe attacks such as adversarial example attacks, imposing threats to tram safety. Only if adversarial attacks are studied thoroughly, researchers can come up with better defence methods against them. However, most existing methods of generating adversarial examples have been devoted to classification, and none of them target tram environment perception systems. In this paper, we propose an improved projected gradient descent (PGD) algorithm and an improved Carlini and Wagner (C&W) algorithm to generate adversarial examples against Faster R-CNN object detectors. Experiments verify that both algorithms can successfully conduct nontargeted and targeted white-box digital attacks when trams are running. We also compare the performance of the two methods, including attack effects, similarity to clean images, and the generating time. The results show that both algorithms can generate adversarial examples within 220 seconds, a much shorter time, without decrease of the success rate.

Quantification of Uncertainties in Deep Learning-based Environment Perception

2021 ◽  
Author(s):  
Marco Braun ◽  
Moritz Luszek ◽  
Jan Siegemund ◽  
Kevin Kollek ◽  
Anton Kummert
Keyword(s):  
Deep Learning ◽  
Environment Perception

Multi-modal haptic image recognition based on deep learning

Sensor Review ◽  
2018 ◽  
Vol 38 (4) ◽  
pp. 486-493 ◽  
Author(s):  
Dong Han ◽  
Hong Nie ◽  
Jinbao Chen ◽  
Meng Chen ◽  
Zhen Deng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Recognition Performance ◽  
Learning Model ◽  
Content Type ◽  
Modal Data ◽  
Environment Perception ◽  
Different Dimensions ◽  
Deep Learning Model ◽  
Practical Implications ◽  
Intelligent Robotics

Purpose This paper aims to improve the diversity and richness of haptic perception by recognizing multi-modal haptic images. Design/methodology/approach First, the multi-modal haptic data collected by BioTac sensors from different objects are pre-processed, and then combined into haptic images. Second, a multi-class and multi-label deep learning model is designed, which can simultaneously learn four haptic features (hardness, thermal conductivity, roughness and texture) from the haptic images, and recognize objects based on these features. The haptic images with different dimensions and modalities are provided for testing the recognition performance of this model. Findings The results imply that multi-modal data fusion has a better performance than single-modal data on tactile understanding, and the haptic images with larger dimension are conducive to more accurate haptic measurement. Practical implications The proposed method has important potential application in unknown environment perception, dexterous grasping manipulation and other intelligent robotics domains. Originality/value This paper proposes a new deep learning model for extracting multiple haptic features and recognizing objects from multi-modal haptic images.

scholarly journals Deep Learning Based Multi-Modal Fusion Architectures for Maritime Vessel Detection

2020 ◽  
Vol 12 (16) ◽  
pp. 2509
Author(s):  
Fahimeh Farahnakian ◽  
Jukka Heikkonen
Keyword(s):  
Deep Learning ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Camera Motion ◽  
Sea Waves ◽  
Illumination Changes ◽  
Infrared Cameras ◽  
Maritime Environment ◽  
Level Fusion ◽  
Different Levels

Object detection is a fundamental computer vision task for many real-world applications. In the maritime environment, this task is challenging due to varying light, view distances, weather conditions, and sea waves. In addition, light reflection, camera motion and illumination changes may cause to false detections. To address this challenge, we present three fusion architectures to fuse two imaging modalities: visible and infrared. These architectures can provide complementary information from two modalities in different levels: pixel-level, feature-level, and decision-level. They employed deep learning for performing fusion and detection. We investigate the performance of the proposed architectures conducting a real marine image dataset, which is captured by color and infrared cameras on-board a vessel in the Finnish archipelago. The cameras are employed for developing autonomous ships, and collect data in a range of operation and climatic conditions. Experiments show that feature-level fusion architecture outperforms the state-of-the-art other fusion level architectures.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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