Three Applications of Deep Learning Algorithms for Object Detection in Satellite Imagery

Napiorkowska, Milena; Petit, David; Marti, Paula

doi:10.1109/igarss.2018.8518102

Cited by 18 publications

(3 citation statements)

References 2 publications

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“…The deep convolutional neural network (DCNN) has made great achievements in image object detection after face recognition. In recent years, a large number of efficient object detection algorithms based on deep learning have emerged successively, such as the region-convolutional neural network (R-CNN), fast region-convolutional neural network (Fast R-CNN), faster region-convolutional neural network (Faster R-CNN), You only look once (YOLO), and Single Shot Multi-Box Detector (SSD) [18]. These algorithms are divided into two categories according to whether there is a region proposal.…”

Section: Object Detectionmentioning

confidence: 99%

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Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology

Zhou

Wu³

et al. 2019

Electronics

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The traditional oil well monitoring method relies on manual acquisition and various high-precision sensors. Using the indicator diagram to judge the working condition of the well is not only difficult to establish but also consumes huge manpower and financial resources. This paper proposes the use of computer vision in the detection of working conditions in oil extraction. Combined with the advantages of an unmanned aerial vehicle (UAV), UAV aerial photography images are used to realize real-time detection of on-site working conditions by real-time tracking of the working status of the head working and other related parts of the pumping unit. Considering the real-time performance of working condition detection, this paper proposes a framework that combines You only look once version 3 (YOLOv3) and a sort algorithm to complete multi-target tracking in the form of tracking by detection. The quality of the target detection in the framework is the key factor affecting the tracking effect. The experimental results show that a good detector makes the tracking speed achieve the real-time effect and provides help for the real-time detection of the working condition, which has a strong practical application.

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Section: Object Detectionmentioning

confidence: 99%

“…The network architecture of RPN is shown in Figure 2. network (Faster R-CNN), You only look once (YOLO), and Single Shot Multi-Box Detector (SSD) [18]. These algorithms are divided into two categories according to whether there is a region proposal.…”

Section: Faster R-cnnmentioning

confidence: 99%

Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology

Zhou

Wu³

et al. 2019

Electronics

View full text Add to dashboard Cite

show abstract

“…These methods also need a new set of parameter configuration when the scene changes. With the availability of high power processors and larger computer memories, the researchers have found opportunities to train deep learning networks which can learn how to identify and segment road segments automatically (Henry et al, 2018, Napiorkowska et al, 2018, Gao et al, 2019, Shi et al, 2018. The main advantage of these artificial intelligence based methods are their capabilities to find the optimal parameter set (the deep neural network weights) which can robustly extract the pixels which are the most likely to come from road segments.…”

Section: Introductionmentioning

confidence: 99%

Rlsnake: A Hybrid Reinforcement Learning Approach for Road Detection

Botteghi

Sırmaçek

Ünsalan

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Road network detection from very high resolution satellite and aerial images is highly important for diverse domains. Although an expert can label road pixels in a given image, this operation is prone to error and quite time consuming remembering that road maps must be updated regularly. Therefore, various computer vision based automated algorithms have been proposed in the last two decades. Nevertheless, due to the diversity of scenes, the field is still open for robust methods which might detect roads on different resolution images of different type of environments. In this study, we picked an earlier proposed road detection method which works based on traditional computer vision and probability theory algorithms. We improved it by further steps using reinforcement learning theory. With the help of the novel hybrid technique (traditional computer vision method combined with reinforcement learning based artificial intelligence), we achieved a solution that we call RLSnake. This new method can learn new image scenes and resolutions rapidly and can work reliably. We believe that the proposed RLSnake will be a significant step in the remote sensing field in order to develop solutions which might increase performance by combining the power of traditional and new techniques.

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Light-Weight Deep Learning Framework for Automated Remote Sensing Images Classification

Adegun

Viriri

Tapamo

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Three Applications of Deep Learning Algorithms for Object Detection in Satellite Imagery

Cited by 18 publications

References 2 publications

Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology

Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology

Rlsnake: A Hybrid Reinforcement Learning Approach for Road Detection

Light-Weight Deep Learning Framework for Automated Remote Sensing Images Classification

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