131 research outputs found
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission
Low-bandwidth communication, such as underwater acoustic communication, is
limited by best-case data rates of 30--50 kbit/s. This renders such channels
unusable or inefficient at best for single image, video, or other
bandwidth-demanding sensor-data transmission. To combat data-transmission
bottlenecks, we consider practical use-cases within the maritime domain and
investigate the prospect of Single Image Super-Resolution methodologies. This
is investigated on a large, diverse dataset obtained during years of trawl
fishing where cameras have been placed in the fishing nets. We propose
down-sampling images to a low-resolution low-size version of about 1 kB that
satisfies underwater acoustic bandwidth requirements for even several frames
per second. A neural network is then trained to perform up-sampling, trying to
reconstruct the original image. We aim to investigate the quality of
reconstructed images and prospects for such methods in practical use-cases in
general. Our focus in this work is solely on learning to reconstruct the
high-resolution images on "real-world" data. We show that our method achieves
better perceptual quality and superior reconstruction than generic bicubic
up-sampling and motivates further work in this area for underwater
applications
Full-Scale Continuous Synthetic Sonar Data Generation with Markov Conditional Generative Adversarial Networks
Deployment and operation of autonomous underwater vehicles is expensive and
time-consuming. High-quality realistic sonar data simulation could be of
benefit to multiple applications, including training of human operators for
post-mission analysis, as well as tuning and validation of autonomous target
recognition (ATR) systems for underwater vehicles. Producing realistic
synthetic sonar imagery is a challenging problem as the model has to account
for specific artefacts of real acoustic sensors, vehicle altitude, and a
variety of environmental factors. We propose a novel method for generating
realistic-looking sonar side-scans of full-length missions, called Markov
Conditional pix2pix (MC-pix2pix). Quantitative assessment results confirm that
the quality of the produced data is almost indistinguishable from real.
Furthermore, we show that bootstrapping ATR systems with MC-pix2pix data can
improve the performance. Synthetic data is generated 18 times faster than real
acquisition speed, with full user control over the topography of the generated
data.Comment: 6 pages, 6 figures. Accepted to ICRA2020. 2020 IEEE International
Conference on Robotics and Automatio
Quality Enhancement for Underwater Images using Various Image Processing Techniques: A Survey
Underwater images are essential to identify the activity of underwater objects. It played a vital role to explore and utilizing aquatic resources. The underwater images have features such as low contrast, different noises, and object imbalance due to lack of light intensity. CNN-based in-deep learning approaches have improved underwater low-resolution photos during the last decade. Nevertheless, still, those techniques have some problems, such as high MSE, PSNT and high SSIM error rate. They solve the problem using different experimental analyses; various methods are studied that effectively treat different underwater image distorted scenes and improve contrast and color deviation compared to other algorithms. In terms of the color richness of the resulting images and the execution time, there are still deficiencies with the latest algorithm. In future work, the structure of our algorithm will be further adjusted to shorten the execution time, and optimization of the color compensation method under different color deviations will also be the focus of future research. With the wide application of underwater vision in different scientific research fields, underwater image enhancement can play an increasingly significant role in the process of image processing in underwater research and underwater archaeology. Most of the target images of the current algorithms are shallow water images. When the artificial light source is added to deep water images, the raw images will face more diverse noises, and image enhancement will face more challenges. As a result, this study investigates the numerous existing systems used for quality enhancement of underwater mages using various image processing techniques. We find various gaps and challenges of current systems and build the enhancement of this research for future improvement. Aa a result of this overview is to define the future problem statement to enhance this research and overcome the challenges faced by previous researchers. On other hand also improve the accuracy in terms of reducing MSE and enhancing PSNR etc
Deep Learning Approaches for Seagrass Detection in Multispectral Imagery
Seagrass forms the basis for critically important marine ecosystems. Seagrass is an important factor to balance marine ecological systems, and it is of great interest to monitor its distribution in different parts of the world. Remote sensing imagery is considered as an effective data modality based on which seagrass monitoring and quantification can be performed remotely. Traditionally, researchers utilized multispectral satellite images to map seagrass manually. Automatic machine learning techniques, especially deep learning algorithms, recently achieved state-of-the-art performances in many computer vision applications. This dissertation presents a set of deep learning models for seagrass detection in multispectral satellite images. It also introduces novel domain adaptation approaches to adapt the models for new locations and for temporal image series. In Chapter 3, I compare a deep capsule network (DCN) with a deep convolutional neural network (DCNN) for seagrass detection in high-resolution multispectral satellite images. These methods are tested on three satellite images in Florida coastal areas and obtain comparable performances. In addition, I also propose a few-shot deep learning strategy to transfer knowledge learned by DCN from one location to the others for seagrass detection. In Chapter 4, I develop a semi-supervised domain adaptation method to generalize a trained DCNN model to multiple locations for seagrass detection. First, the model utilizes a generative adversarial network (GAN) to align marginal distribution of data in the source domain to that in the target domain using unlabeled data from both domains. Second, it uses a few labeled samples from the target domain to align class-specific data distributions between the two. The model achieves the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods. In Chapter 5, I develop a semantic segmentation method for seagrass detection in multispectral time-series images. First, I train a state-of-the-art image segmentation method using an active learning approach where I use the DCNN classifier in the loop. Then, I develop an unsupervised domain adaptation (UDA) algorithm to detect seagrass across temporal images. I also extend our unsupervised domain adaptation work for seagrass detection across locations. In Chapter 6, I present an automated bathymetry estimation model based on multispectral satellite images. Bathymetry refers to the depth of the ocean floor and contributes a predominant role in identifying marine species in seawater. Accurate bathymetry information of coastal areas will facilitate seagrass detection by reducing false positives because seagrass usually do not grow beyond a certain depth. However, bathymetry information of most parts of the world is obsolete or missing. Traditional bathymetry measurement systems require extensive labor efforts. I utilize an ensemble machine learning-based approach to estimate bathymetry based on a few in-situ sonar measurements and evaluate the proposed model in three coastal locations in Florida
Generative neural data synthesis for autonomous systems
A significant number of Machine Learning methods for automation currently rely on
data-hungry training techniques. The lack of accessible training data often represents
an insurmountable obstacle, especially in the fields of robotics and automation, where
acquiring new data can be far from trivial. Additional data acquisition is not only often
expensive and time-consuming, but occasionally is not even an option. Furthermore,
the real world applications sometimes have commercial sensitivity issues associated
with the distribution of the raw data.
This doctoral thesis explores bypassing the aforementioned difficulties by synthesising new realistic and diverse datasets using the Generative Adversarial Network (GAN).
The success of this approach is demonstrated empirically through solving a variety of
case-specific data-hungry problems, via application of novel GAN-based techniques
and architectures.
Specifically, it starts with exploring the use of GANs for the realistic simulation of
the extremely high-dimensional underwater acoustic imagery for the purpose of training
both teleoperators and autonomous target recognition systems. We have developed a
method capable of generating realistic sonar data of any chosen dimension by image-translation GANs with Markov principle.
Following this, we apply GAN-based models to robot behavioural repertoire generation, that enables a robot manipulator to successfully overcome unforeseen impedances,
such as unknown sets of obstacles and random broken joints scenarios.
Finally, we consider dynamical system identification for articulated robot arms. We
show how using diversity-driven GAN models to generate exploratory trajectories can
allow dynamic parameters to be identified more efficiently and accurately than with
conventional optimisation approaches.
Together, these results show that GANs have the potential to benefit a variety of
robotics learning problems where training data is currently a bottleneck
OBJECT PERCEPTION IN UNDERWATER ENVIRONMENTS: A SURVEY ON SENSORS AND SENSING METHODOLOGIES
Underwater robots play a critical role in the marine industry. Object perception is the foundation for the automatic
operations of submerged vehicles in dynamic aquatic environments. However, underwater perception
encounters multiple environmental challenges, including rapid light attenuation, light refraction, or backscattering
effect. These problems reduce the sensing devices’ signal-to-noise ratio (SNR), making underwater
perception a complicated research topic. This paper describes the state-of-the-art sensing technologies and
object perception techniques for underwater robots in different environmental conditions. Due to the current
sensing modalities’ various constraints and characteristics, we divide the perception ranges into close-range,
medium-range, and long-range. We survey and describe recent advances for each perception range and suggest
some potential future research directions worthy of investigating in this field
Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation
Single-Image Super-Resolution can support robotic tasks in environments where
a reliable visual stream is required to monitor the mission, handle
teleoperation or study relevant visual details. In this work, we propose an
efficient Generative Adversarial Network model for real-time Super-Resolution.
We adopt a tailored architecture of the original SRGAN and model quantization
to boost the execution on CPU and Edge TPU devices, achieving up to 200 fps
inference. We further optimize our model by distilling its knowledge to a
smaller version of the network and obtain remarkable improvements compared to
the standard training approach. Our experiments show that our fast and
lightweight model preserves considerably satisfying image quality compared to
heavier state-of-the-art models. Finally, we conduct experiments on image
transmission with bandwidth degradation to highlight the advantages of the
proposed system for mobile robotic applications
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