Threshold Based Skin Color Classification

In this paper, we presented a new formula for skin classification. The proposed formula can overcome sensitivity to noise. Our approach was based multi-skin color Hue, Saturation, and Value color space and multi-level segmentation. Skin regions were extracted using three skin color classes, namely the Caucasoid, Mongolid and Nigroud. Moreover, in this formula, we adopted Gaussian-based weight k-NN algorithm for skin classification. The experiment result shows that the best result was achieved for Caucasoid class with 84.29 percent fmeasure

Drone-based non-destructive inspection of industrial sites: a review and case studies

Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented

Evaluation and selection of video stabilization techniques for UAV-based active infrared thermography application

nmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system

Diagnosis of composite materials in aircraft applications: towards a UAV active thermography inspection approach

Diagnosis and prognosis of failures for aircrafts’ integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. This paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of active thermography techniques in the aerospace industry. Furthermore, as the use of unmanned aerial vehicles (UAVs) for the remote inspection of large and/or difficult access areas has significantly grown in the last few years thanks to their flexibility of flight and to the possibility to carry one or several measuring sensors, the aim to use a UAV active thermography system for the inspection of large composite aeronautical structures in a continuous dynamic mode is proposed

Drone-Based Non-Destructive Inspection of Industrial Sites: A Review and Case Studies

Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented

Aerial inspection of complex structures using multi-modal procedures and data processing a comprehensive solution for drone-based multi-modal inspection of industrial components

Thèse ou mémoire avec insertion d'articlesLes systèmes aériens autonomes (UAV/UAS), communément appelés drones, sont un sujet de plus en plus important dans les inspections par essais non-destructifs (END). Avec les avancées technologiques significatives des caméras thermiques, les méthodes d'inspection visuelle acquièrent continuellement de l'attention dans les inspections END. Les inspections dans les zones difficiles d'accès sont coûteuses, parfois impossibles en raison de la nature de la zone ou des dangers possibles pour les ressources humaines. L'inspection de spécimens complexes et de grande taille, notamment les des structures courbes, nécessite des relevés approfondis sous différents aspects, ce qui est presque impossible ou très coûteux avec des véhicules terrestres ou des ressources humaines. Ainsi, en raison de leur grande manœuvrabilité, les industries investissent davantage dans les drones pour surmonter les problèmes mentionnés et aider les inspecteurs à examiner les composants de manière approfondie. De plus, grâce à des développements récents, les UAVs peuvent également accéder à des zones éloignées ou difficiles d'accès et transporter de nombreuses charges utiles. Malgré les énormes avantages de l'utilisation des drones pour l'inspection, certains défis doivent être relevés. Ces dernières années, de nombreuses études se sont concentrées sur l'utilisation d'images thermiques/visibles pour inspecter différentes structures. Cependant, l'utilisation de données d'inspection multimodales par drone, y compris les données d'imagerie visible, thermique et de profondeur, pour fournir une compréhension approfondie de l'échantillon et de son environnement afin de produire une analyse plus précise, doit être étudiée en détail. Tout d'abord, cette étude aborde les défis communs des inspections par drone. La détection de l'effet de la réflexion thermique dans une inspection thermographique est le premier défi abordé dans cette étude. Ensuite, l'effet des mouvements constants et soudains d'un drone sur l'analyse des séquences d'images thermiques est étudié de manière approfondie. En outre, les résultats sont évalués à l'aide d'un scénario d'utilisation où le drone surveille un endroit fixe tout en restant en vol stationnaire. Par la suite, cette étude vise à développer une plateforme multi-sensorielle comprenant une structure de montage, des capteurs d'imagerie et un ordinateur embarqué. La solution logicielle intégrée à cette plate-forme fournit les fonctions requises d'acquisition, de transmission, de stockage et de traitement des données. De plus, cette étude se concentre sur le traitement de modalités multiples ou individuelles. Notamment, une méthode de segmentation par auto-apprentissage est proposée dans le contexte de la détection de défauts dans les images thermiques. Aussi, un algorithme de détection de fissures par drone est présenté pour analyser l'inspection visuelle des chaussées et des structures en béton. Ensuite, cette étude s'est concentrée sur le traitement des données multi-modales acquises par la plateforme multi-sensorielle présentée. En effet, l'utilisation d'images thermiques et visibles couplées pour améliorer la détection des anomalies est étudiée de manière approfondie. Plusieurs scénarios d'utilisation sont introduits présentant différentes approches pour améliorer l'efficacité de la détection. Ces derniers fournissent un aperçu de l'applicabilité des sous-études introduites. Pour chacun d'entre eux, de multiples expériences sont menées pour démontrer les applications des méthodes proposées dans des scénarios de cas réels.Unmanned Aerial Vehicles/Systems (UAVs/UAS), commonly known as drones, is a rising topic in Non-Destructive Testing (NDT) inspections. With significant technological advancements in thermal cameras, visual inspection methods continuously gain much attention in non-destructive inspections. Inspections in remote or hard-to-access areas are costly and sometimes impossible due to the area's nature or the possible dangers facing human resources. Inspection of complex and large specimens, especially with curvaceous structures, requires extensive surveys from different aspects, which is nearly impossible or very costly using ground vehicles or human resources. Thus, industries are investing more in drones to overcome mentioned problems as they have high flexibility of maneuver, which can assist inspectors in examining the components thoroughly. They can also access remote or hard-to-access areas and carry many payloads thanks to recent developments. Despite the enormous benefits of using drones for inspection, some challenges need to be addressed. In recent years, many studies focused on using thermal/visible images to inspect different structures. However, using multi-modal data, including visible, thermal, and depth imagery data, provides an extensive understanding of the specimen and surrounding environment in case of drone-enabled inspections and produces a more accurate analysis that needs to be thoroughly studied. Firstly, this study addresses the common challenges in drone-based inspections in the scope of this research. Detecting the effect of thermal reflection in a thermographic inspection is the first challenge addressed in this study. Later, the effect of a drone's constant and sudden motions on analyzing thermal image sequences is investigated comprehensively. Also, the results are evaluated using a use-case scenario where the drone monitors a fixed location while hovering. Also, the next part of this study aims to develop a multi-sensory platform, including a mounting structure, imagery sensors, and an onboard computer. The software solution embedded in this platform provides the required data acquisition, transmission, storage, and processing features. Later, this study focuses on the processing of multiple or individual modalities. Firstly, a self-training segmentation method is proposed in the context of defect detection in thermal images. Also, a drone-enabled crack detection algorithm is presented for analyzing the visual inspection of pavement and concrete structures. Next, this study focused on processing multi-modal data acquired by the presented multi-sensory platform. Firstly, using coupled thermal and visible images to enhance abnormality detection is investigated thoroughly. Several use-case scenarios are introduced, presenting different approaches to enhance the detection's efficiency. In order to provide insight into the applicability of the introduced sub-studies. For each of them, multiple experiments are conducted demonstrating the applications of the proposed methods in real-case scenarios

A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis

The recent development of gas imaging technologies has raised a growing interest for various applications. Gas imaging can significantly enhance functional safety by early detection of hazardous gas leaks. Moreover, optical gas imaging technologies can be used to identify possible gas leakages and to investigate the amount of gas emission in industrial sites, which is essential, primarily based on current efforts to decrease greenhouse gas emissions all around the world. Therefore, exploring the solutions for automating the inspection process can persuade industries for more regular tests and monitoring. One of the main challenges in gas imaging is the proximity condition required for data to be more reliable for analysis. Therefore, the use of unmanned aerial vehicles can be very advantageous as they can provide significant access due to their maneuver capabilities. Despite the advantages of using drones, their movements and sudden motions during hovering can diminish data usability. In this paper, we propose a method for gas leak detection and visually-enhancement of gas emanation involving stabilization and gas leak detection steps. In addition, a comparative analysis of candidate stabilization techniques is conducted to find the most suitable technique for the drone-based application. Moreover, the system is evaluated using three experiments respectively on an isolated environment, a real scenario, and a drone-based inspection

A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis

The recent development of gas imaging technologies has raised a growing interest for various applications. Gas imaging can significantly enhance functional safety by early detection of hazardous gas leaks. Moreover, optical gas imaging technologies can be used to identify possible gas leakages and to investigate the amount of gas emission in industrial sites, which is essential, primarily based on current efforts to decrease greenhouse gas emissions all around the world. Therefore, exploring the solutions for automating the inspection process can persuade industries for more regular tests and monitoring. One of the main challenges in gas imaging is the proximity condition required for data to be more reliable for analysis. Therefore, the use of unmanned aerial vehicles can be very advantageous as they can provide significant access due to their maneuver capabilities. Despite the advantages of using drones, their movements and sudden motions during hovering can diminish data usability. In this paper, we propose a method for gas leak detection and visually-enhancement of gas emanation involving stabilization and gas leak detection steps. In addition, a comparative analysis of candidate stabilization techniques is conducted to find the most suitable technique for the drone-based application. Moreover, the system is evaluated using three experiments respectively on an isolated environment, a real scenario, and a drone-based inspection