A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Carrio, Adrian; Sampedro, Carlos; Rodríguez-Ramos, Alejandro; Campoy, Pascual

doi:10.1155/2017/3296874

Cited by 285 publications

(163 citation statements)

References 53 publications

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“…Figure 3d shows the set of contours L found for σ = [1,2,3]. Besides, it is also appreciated that at a fine scale ( Fig.…”

Section: Non-accidentalness Estimationmentioning

confidence: 77%

“…Recently, convolutional neural networks (CNN) techniques offer the possibility of detecting an object from a large set of classes with a high-reliability [3]. Nevertheless, these methods must have been trained with a database containing the object classes in a wide range of situations and, in case of changes in the object or the scene, the database must be rebuilt [19] [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised Perception Model for UAVs Landing Target Detection and Recognition

Bazan

Dokládal

Dokladalova

2018

Advanced Concepts for Intelligent Vision Systems

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Abstract. Today, unmanned aerial vehicles (UAV) play an interesting role in the so-called Industry 4.0. One of many problems studied by companies and research groups are the sensing of the environment intelligently. In this context, we tackle the problem of autonomous landing, and more precisely, the robust detection and recognition of a unique landing target in an outdoor environment. The challenge is how to deal with images under non-controlled light conditions impacted by shadows, change of scale, perspective, vibrations, noise, blur, among others. In this paper, we introduce a robust unsupervised model allowing to detect and recognize a target, in a perceptual-inspired manner, using the Gestalt principles of non-accidentalness and grouping. Our model extracts the landing target contours as outliers using the RX anomaly detector and computing proximity and a similarity measure. Finally, we show the use of error correction Hamming code to reduce the recognition errors.

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“…Figure 3d shows the set of contours L found for σ = [1,2,3]. Besides, it is also appreciated that at a fine scale ( Fig.…”

Section: Non-accidentalness Estimationmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Unsupervised Perception Model for UAVs Landing Target Detection and Recognition

Bazan

Dokládal

Dokladalova

2018

Advanced Concepts for Intelligent Vision Systems

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“…More recently, (Carrio et al, 2017) provided a thorough review on the reported uses and applications of DL for UAVs, including the major challenges for the application of DL for UAV-based solutions. Although the UAVs can be equipped with a variety of sensor payloads (such as LiDAR and other nondestructive sensing devices), (Carrio et al, 2017) found that most successful applications of DCNN to UAVs were with respect to visual data (images) owing to the low-cost, lightweight, and low power consumption of image sensors.…”

Section: Deep Learning In Uav Based Visual Inspection: a Brief Reviewmentioning

confidence: 99%

“…Although the UAVs can be equipped with a variety of sensor payloads (such as LiDAR and other nondestructive sensing devices), (Carrio et al, 2017) found that most successful applications of DCNN to UAVs were with respect to visual data (images) owing to the low-cost, lightweight, and low power consumption of image sensors.…”

Section: Deep Learning In Uav Based Visual Inspection: a Brief Reviewmentioning

confidence: 99%

Crack Damage Detection in Unmanned Aerial Vehicle Images of Civil Infrastructure Using Pre-Trained Deep Learning Model

2018

IJTTE

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Abstract:The field of computer vision based civil infrastructure defect detection is constantly evolving with steady advances being made in sensing technologies, hardware, and image processing techniques. Although a number of image processing techniques have been proposed over the years with varying degrees of success, real-world situations (e.g., lack of background illumination, shadow changes) pose a significant challenge to the wide adoption of such techniques, especially for routine analysis. With the emerging application of Unmanned Aerial Vehicles (UAVs) or drones for civil infrastructure inspection and condition monitoring, an added level of complexity is introduced in vision-based crack detection as the process of acquiring images using UAVs is not yet standardized resulting in images of widely varying sizes, resolutions, blurriness, etc. In recent years, Deep Learning, a generalized form of Deep Neural Network (DNN) algorithms that can learn very complex mappings between inputs and outputs directly from the data, has achieved huge success in diverse fields such as automatic speech recognition, image recognition, Natural Language Processing (NLP), drug and materials discovery, etc. However, the large number of hidden neurons and layers used in DNNs result in computationally-intensive matrix and vector computations involving millions of parameters, requiring the use of high-performance computing systems. Also, it is practically impossible to get labeled "big data" samples in many domains to be able to train an entire DNN from scratch. In such situations, the use of a pre-trained deep learning model and fine-tuning it to the novel task at hand with smaller datasets, has shown to be successful across domains. In this paper, we propose the use of pre-trained deep learning models with transfer learning for crack damage detection in UAV images of civil infrastructure. The robustness of the proposed approach is tested on a small set of real-world, complex UAV-sourced infrastructure images not used during training and validation. The results show that the proposed method can rapidly and easily achieve up to 90% accuracy in finding cracks in realistic situations without any augmentation and preprocessing.

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“…For the flight control of UAVs, the inputs of the learning can be images, light imaging detection and ranging sensor data, or both [226]. Learning the pattern from such inputs can allow to learn proper controlling schemes even under unknown circumstances.…”

mentioning

confidence: 99%

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Shakeri

Al-Garadi

Badawy

et al. 2019

IEEE Commun. Surv. Tutorials

188

100

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Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas. M 2 respectively. However, as shown in Fig.1, both technologies provide two extremes for surveillance with respect to accuracy versus intrusive deployment and maintenance. SRS provides slow large-scale monitoring, while CSNs require widespread deployment and constant human intervention for setup and maintenance, which may not be feasible particularly in harsh environments. On the one hand, SRS cannot be used for finegrain monitoring, such as monitoring of structural objects, with high accuracy, which limits its usage in many applications that require close monitoring and context detection [5]. On the other hand, traditional sensor networks (SNs) are spreading in countless number of Internet of Things (IoT) applications to provide automatic monitoring of objects, humans, and/or infrastructure. SNs, however, require permanent deployment of large magnitude of localized sensors, intrusively to the environment in many cases, and also require constant human intervention to manage and maintain. The proliferation of lowcost UAVs [6] is emerging to provide innovative ways of low altitude sensing with zero-deployment (i.e., no fixed deployment requirements) that can fundamentally change the way such applications are designed.Similar to surveillance applications, UAV technologies have progressed significantly in several applications in the past decade, making commercial and civil applications feasible and compelling. As it occurs with many disruptive technolo...

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A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Cited by 285 publications

References 53 publications

Unsupervised Perception Model for UAVs Landing Target Detection and Recognition

Unsupervised Perception Model for UAVs Landing Target Detection and Recognition

Crack Damage Detection in Unmanned Aerial Vehicle Images of Civil Infrastructure Using Pre-Trained Deep Learning Model

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

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