Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement diseases that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement disease detection. The source codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}.Comment: Revision on IEEE Trans on IT

Deep Domain Adaptation for Pavement Crack Detection

Deep learning-based pavement cracks detection methods often require large-scale labels with detailed crack location information to learn accurate predictions. In practice, however, crack locations are very difficult to be manually annotated due to various visual patterns of pavement crack. In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns to take advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available. Specifically, DDACDN first extracts crack features from both the source and target domain by a two-branch weights-shared backbone network. And in an effort to achieve the cross-domain adaptation, an intermediate domain is constructed by aggregating the three-scale features from the feature space of each domain to adapt the crack features from the source domain to the target domain. Finally, the network involves the knowledge of both domains and is trained to recognize and localize pavement cracks. To facilitate accurate training and validation for domain adaptation, we use two challenging pavement crack datasets CQU-BPDD and RDD2020. Furthermore, we construct a new large-scale Bituminous Pavement Multi-label Disease Dataset named CQU-BPMDD, which contains 38994 high-resolution pavement disease images to further evaluate the robustness of our model. Extensive experiments demonstrate that DDACDN outperforms state-of-the-art pavement crack detection methods in predicting the crack location on the target domain.Comment: 12 pages, 10 figure

Deep convolutional generative adversarial network-based synthesis of datasets for road pavement distress segmentation

В данной работе рассматривается комплекс задач обнаружения различных дефектов дорожного полотна автомобильных дорог и современные методы их решения. Представленное сравнение общедоступных наборов данных позволяет сделать вывод о сложности и малой разработанности задачи сегментации дефектов дорожного покрытия по изображению общего вида автомобильных дорог. Для решения данной проблемы разработаны алгоритмы генерации синтетического набора данных для сегментации дефектов классов трещин и выбоин на основе методов компьютерной графики и генеративносостязательных сетей. Проведено сравнение точности сегментации дефектов дорожного покрытия полносверточной нейронной сетью U-Net на реальном и комбинированных наборах данных

Coping with Data Scarcity in Deep Learning and Applications for Social Good

The recent years are experiencing an extremely fast evolution of the Computer Vision and Machine Learning fields: several application domains benefit from the newly developed technologies and industries are investing a growing amount of money in Artificial Intelligence. Convolutional Neural Networks and Deep Learning substantially contributed to the rise and the diffusion of AI-based solutions, creating the potential for many disruptive new businesses. The effectiveness of Deep Learning models is grounded by the availability of a huge amount of training data. Unfortunately, data collection and labeling is an extremely expensive task in terms of both time and costs; moreover, it frequently requires the collaboration of domain experts. In the first part of the thesis, I will investigate some methods for reducing the cost of data acquisition for Deep Learning applications in the relatively constrained industrial scenarios related to visual inspection. I will primarily assess the effectiveness of Deep Neural Networks in comparison with several classical Machine Learning algorithms requiring a smaller amount of data to be trained. Hereafter, I will introduce a hardware-based data augmentation approach, which leads to a considerable performance boost taking advantage of a novel illumination setup designed for this purpose. Finally, I will investigate the situation in which acquiring a sufficient number of training samples is not possible, in particular the most extreme situation: zero-shot learning (ZSL), which is the problem of multi-class classification when no training data is available for some of the classes. Visual features designed for image classification and trained offline have been shown to be useful for ZSL to generalize towards classes not seen during training. Nevertheless, I will show that recognition performances on unseen classes can be sharply improved by learning ad hoc semantic embedding (the pre-defined list of present and absent attributes that represent a class) and visual features, to increase the correlation between the two geometrical spaces and ease the metric learning process for ZSL. In the second part of the thesis, I will present some successful applications of state-of-the- art Computer Vision, Data Analysis and Artificial Intelligence methods. I will illustrate some solutions developed during the 2020 Coronavirus Pandemic for controlling the disease vii evolution and for reducing virus spreading. I will describe the first publicly available dataset for the analysis of face-touching behavior that we annotated and distributed, and I will illustrate an extensive evaluation of several computer vision methods applied to the produced dataset. Moreover, I will describe the privacy-preserving solution we developed for estimating the \u201cSocial Distance\u201d and its violations, given a single uncalibrated image in unconstrained scenarios. I will conclude the thesis with a Computer Vision solution developed in collaboration with the Egyptian Museum of Turin for digitally unwrapping mummies analyzing their CT scan, to support the archaeologists during mummy analysis and avoiding the devastating and irreversible process of physically unwrapping the bandages for removing amulets and jewels from the body

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Rich probabilistic models for semantic labeling

Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung

Deep Learning Methods for 3D Aerial and Satellite Data

Recent advances in digital electronics have led to an overabundance of observations from electro-optical (EO) imaging sensors spanning high spatial, spectral and temporal resolution. This unprecedented volume, variety, and velocity is overwhelming our capacity to manage and translate that data into actionable information. Although decades of image processing research have taken the human out of the loop for many important tasks, the human analyst is still an irreplaceable link in the image exploitation chain, especially for more complex tasks requiring contextual understanding, memory, discernment, and learning. If knowledge discovery is to keep pace with the growing availability of data, new processing paradigms are needed in order to automate the analysis of earth observation imagery and ease the burden of manual interpretation. To address this gap, this dissertation advances fundamental and applied research in deep learning for aerial and satellite imagery. We show how deep learning---a computational model inspired by the human brain---can be used for (1) tracking, (2) classifying, and (3) modeling from a variety of data sources including full-motion video (FMV), Light Detection and Ranging (LiDAR), and stereo photogrammetry. First we assess the ability of a bio-inspired tracking method to track small targets using aerial videos. The tracker uses three kinds of saliency maps: appearance, location, and motion. Our approach achieves the best overall performance, including being the only method capable of handling long-term occlusions. Second, we evaluate the classification accuracy of a multi-scale fully convolutional network to label individual points in LiDAR data. Our method uses only the 3D-coordinates and corresponding low-dimensional spectral features for each point. Evaluated using the ISPRS 3D Semantic Labeling Contest, our method scored second place with an overall accuracy of 81.6\%. Finally, we validate the prediction capability of our neighborhood-aware network to model the bare-earth surface of LiDAR and stereo photogrammetry point clouds. The network bypasses traditionally-used ground classifications and seamlessly integrate neighborhood features with point-wise and global features to predict a per point Digital Terrain Model (DTM). We compare our results with two widely used softwares for DTM extraction, ENVI and LAStools. Together, these efforts have the potential to alleviate the manual burden associated with some of the most challenging and time-consuming geospatial processing tasks, with implications for improving our response to issues of global security, emergency management, and disaster response

Optimization for Deep Learning Systems Applied to Computer Vision

149 p.Since the DL revolution and especially over the last years (2010-2022), DNNs have become an essentialpart of the CV field, and they are present in all its sub-fields (video-surveillance, industrialmanufacturing, autonomous driving, ...) and in almost every new state-of-the-art application that isdeveloped. However, DNNs are very complex and the architecture needs to be carefully selected andadapted in order to maximize its efficiency. In many cases, networks are not specifically designed for theconsidered use case, they are simply recycled from other applications and slightly adapted, without takinginto account the particularities of the use case or the interaction with the rest of the system components,which usually results in a performance drop.This research work aims at providing knowledge and tools for the optimization of systems based on DeepLearning applied to different real use cases within the field of Computer Vision, in order to maximizetheir effectiveness and efficiency

A Review on Deep Learning in UAV Remote Sensing

Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms' applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicles (UAV) based applications have dominated aerial sensing research. However, a literature revision that combines both "deep learning" and "UAV remote sensing" thematics has not yet been conducted. The motivation for our work was to present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery. We focused mainly on describing classification and regression techniques used in recent applications with UAV-acquired data. For that, a total of 232 papers published in international scientific journal databases was examined. We gathered the published material and evaluated their characteristics regarding application, sensor, and technique used. We relate how DL presents promising results and has the potential for processing tasks associated with UAV-based image data. Lastly, we project future perspectives, commentating on prominent DL paths to be explored in the UAV remote sensing field. Our revision consists of a friendly-approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts.Comment: 38 pages, 10 figure