Real-Time, Cloud-Based Object Detection for Unmanned Aerial Vehicles

Lee, Jangwon; Wang, Jingya; Crandall, David J.; Šabanović, Selma; Fox, Geoffrey

doi:10.1109/irc.2017.77

Cited by 111 publications

(46 citation statements)

References 26 publications

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“…In the robotics field, feature extraction systems based on CNN models have been mainly applied for object recognition [42][43][44][45][46][47][48] and scene classification [51][52][53][54]. Concerning the object recognition task, recent advances have integrated object detection solutions by means of bounding box regression and object classification capabilities within the same CNN model [42][43][44].…”

Section: With Image Sensorsmentioning

confidence: 99%

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

et al. 2017

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Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions.

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Section: With Image Sensorsmentioning

confidence: 99%

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

et al. 2017

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“…However, the target platform is a workstation equipped with a GPU which makes this not suitable for embedding in UAVs. Other works such as [4] propose cloud computing as a means to achieve real-time object detection from a UAV. e computation of the object detection algorithm is o -loaded to a web service that runs a CNN to detect di erent objects.…”

Section: Aerial Object Detection Using Cnnsmentioning

confidence: 99%

Efficient ConvNet-based Object Detection for Unmanned Aerial Vehicles by Selective Tile Processing

Plastiras

Kyrkou

Theocharides

2018

Proceedings of the 12th International Conference on Distributed Smart Cameras

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Many applications utilizing Unmanned Aerial Vehicles (UAVs) require the use of computer vision algorithms to analyze the information captured from their on-board camera. Recent advances in deep learning have made it possible to use single-shot Convolutional Neural Network (CNN) detection algorithms that process the input image to detect various objects of interest. To keep the computational demands low these neural networks typically operate on small image sizes which, however, makes it di cult to detect small objects. is is further emphasized when considering UAVs equipped with cameras where due to the viewing range, objects tend to appear relatively small. is paper therefore, explores the trade-o s involved when maintaining the resolution of the objects of interest by extracting smaller patches (tiles) from the larger input image and processing them using a neural network. Speci cally, we introduce an a ention mechanism to focus on detecting objects only in some of the tiles and a memory mechanism to keep track of information for tiles that are not processed. rough the analysis of di erent methods and experiments we show that by carefully selecting which tiles to process we can considerably improve the detection accuracy while maintaining comparable performance to CNNs that resize and process a single image which makes the proposed approach suitable for UAV applications.

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“…Some recent studies, not exclusive to the SHM context, report on the use of DL for UAV-based applications. For instance, (Lee et al, 2017) proposed a novel object detection method wherein the images captured using the UAV are processed in the off-board computing cloud using the state-of-the-art Faster Regions with CNNs (R-CNNs), while low-level object detection and short-term navigation takes place onboard. They tested their approach with a Parrot AR.Drone 2.0 as a low-cost, lightweight drone in an indoor environment and reported that their hybrid approach is able to approach realtime performance even when detecting hundreds of object categories, despite the unpredictable communication lags resulting from the use of cloud-based computation resources.…”

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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Real-Time, Cloud-Based Object Detection for Unmanned Aerial Vehicles

Cited by 111 publications

References 26 publications

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

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

Efficient ConvNet-based Object Detection for Unmanned Aerial Vehicles by Selective Tile Processing

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

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