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A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (25 December 2023) | Viewed by 73107

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Prof. Dr. Seokwon Yeom

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School of AI, Degu University, Gyeongsan 38453, Republic of Korea
Interests: intelligent image processing; deep/machine learning; target tracking
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Special Issue Information

Dear Colleagues,

Recently, the use of drones for various purposes is increasing. Drones can be remotely controlled or programmed to capture scenes from a distance. This capture is cost-effective and does not require highly trained personnel. Drones are widely used in various fields such as video surveillance, wildlife and farm monitoring, industrial investigation, search and rescue, firefighting, and 3D visualization.

Multiple sensors can be mounted on a drone. In addition to conventional visual cameras, infrared thermal imaging and multispectral imaging are available for drones. LiDAR and SAR are active sensors that can be mounted on drones. These mobile aerial imaging sensors provide a new perspective on research and development for a variety of applications. However, more challenges are often posed than grounded cameras because of the unique sensing environments and limited resources of a drone. Certainly, information acquired by a drone is of tremendous value, thus intelligent analysis of them is necessary to make the best use of it.

This Special Issue focuses on a wide range of intelligent processing of images and sensor data acquired by drones. The objectives of intelligent processing range from the refinement of raw data to the symbolic representation and visualization of the real world. This can be achieved through image/signal processing or deep/machine learning algorithms. The latest technological developments will be shared through this Special Issue. Researchers and investigators are invited to contribute original research or review articles in this Special Issue.

Article / Review

Prof. Dr. Seokwon Yeom
Guest Editor

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Keywords

visible / infrared thermal / multispectral image analysis
LiDAR / SAR with a drone
video surveillance
monitoring and inspection
3D imaging and visualization
detection, recognition, and tracking
segmentation and feature extraction
image registration and fusion

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Related Special Issue

Intelligent Image Processing and Sensing for Drones, 2nd Edition in Drones (7 articles)

Published Papers (11 papers)

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Editorial

Jump to: Research, Review

4 pages, 154 KiB

Open AccessEditorial

Special Issue on Intelligent Image Processing and Sensing for Drones

by Seokwon Yeom

Drones 2024, 8(3), 87; https://doi.org/10.3390/drones8030087 - 4 Mar 2024

Viewed by 1747

Abstract

Recently, the use of drones or unmanned aerial vehicles (UAVs) for various purposes has been increasing [...] Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones)

Research

Jump to: Editorial, Review

15 pages, 9694 KiB

Open AccessFeature PaperArticle

Thermal Image Tracking for Search and Rescue Missions with a Drone

by Seokwon Yeom

Drones 2024, 8(2), 53; https://doi.org/10.3390/drones8020053 - 5 Feb 2024

Cited by 4 | Viewed by 6173

Abstract

Infrared thermal imaging is useful for human body recognition for search and rescue (SAR) missions. This paper discusses thermal object tracking for SAR missions with a drone. The entire process consists of object detection and multiple-target tracking. The You-Only-Look-Once (YOLO) detection model is utilized to detect people in thermal videos. Multiple-target tracking is performed via track initialization, maintenance, and termination. Position measurements in two consecutive frames initialize the track. Tracks are maintained using a Kalman filter. A bounding box gating rule is proposed for the measurement-to-track association. This proposed rule is combined with the statistically nearest neighbor association rule to assign measurements to tracks. The track-to-track association selects the fittest track for a track and fuses them. In the experiments, three videos of three hikers simulating being lost in the mountains were captured using a thermal imaging camera on a drone. Capturing was assumed under difficult conditions; the objects are close or occluded, and the drone flies arbitrarily in horizontal and vertical directions. Robust tracking results were obtained in terms of average total track life and average track purity, whereas the average mean track life was shortened in harsh searching environments. Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones)

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23 pages, 191929 KiB

Open AccessEditor’s ChoiceArticle

Transmission Line Segmentation Solutions for UAV Aerial Photography Based on Improved UNet

by Min He, Liang Qin, Xinlan Deng, Sihan Zhou, Haofeng Liu and Kaipei Liu

Drones 2023, 7(4), 274; https://doi.org/10.3390/drones7040274 - 17 Apr 2023

Cited by 8 | Viewed by 2436

Abstract

The accurate and efficient detection of power lines and towers in aerial drone images with complex backgrounds is crucial for the safety of power grid operations and low-altitude drone flights. In this paper, we propose a new method that enhances the deep learning segmentation model UNet algorithm called TLSUNet. We enhance the UNet algorithm by using a lightweight backbone structure to extract the features and then reconstructing them with contextual information features. In this network model, to reduce its parameters and computational complexity, we adopt DFC-GhostNet (Dubbed Full Connected) as the backbone feature extraction network, which is composed of the DFC-GhostBottleneck structure and uses asymmetric convolution to capture long-distance targets in transmission lines, thus enhancing the model’s extraction capability. Additionally, we design a hybrid feature extraction module based on convolution and a transformer to refine deep semantic features and improve the model’s ability to locate towers and transmission lines in complex environments. Finally, we adopt the up-sampling operator CARAFE (Content-Aware Re-Assembly of FEature) to improve segmentation accuracy by enhancing target restoration using contextual neighborhood pixel information correlation under feature decoding. Our experiments on public aerial photography datasets demonstrate that the improved model requires only 8.3% of the original model’s computational effort and has only 21.4% of the original model’s parameters, while achieving a reduction in inference speed delay by 0.012 s. The segmentation metrics also showed significant improvements, with the mIOU improving from 79.75% to 86.46% and the mDice improving from 87.83% to 92.40%. These results confirm the effectiveness of our proposed method. Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones)

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18 pages, 2813 KiB

Open AccessArticle

Non-Linear Signal Processing Methods for UAV Detections from a Multi-Function X-Band Radar

by Mohit Kumar and P. Keith Kelly

Drones 2023, 7(4), 251; https://doi.org/10.3390/drones7040251 - 6 Apr 2023

Cited by 3 | Viewed by 2610

Abstract

This article develops the applicability of non-linear processing techniques such as Compressed Sensing (CS), Principal Component Analysis (PCA), Iterative Adaptive Approach (IAA), and Multiple-input-multiple-output (MIMO) for the purpose of enhanced UAV detections using portable radar systems. The combined scheme has many advantages and the potential for better detection and classification accuracy. Some of the benefits are discussed here with a phased array platform in mind, the novel portable phased array Radar (PWR) by Agile RF Systems (ARS), which offers quadrant outputs. CS and IAA both show promising results when applied to micro-Doppler processing of radar returns owing to the sparse nature of the target Doppler frequencies. This shows promise in reducing the dwell time and increases the rate at which a volume can be interrogated. Real-time processing of target information with iterative and non-linear solutions is possible now with the advent of GPU-based graphics processing hardware. Simulations show promising results. Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones)

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20 pages, 22592 KiB

Open AccessFeature PaperArticle

Segmentation Detection Method for Complex Road Cracks Collected by UAV Based on HC-Unet++

by Hongbin Cao, Yuxi Gao, Weiwei Cai, Zhuonong Xu and Liujun Li

Drones 2023, 7(3), 189; https://doi.org/10.3390/drones7030189 - 10 Mar 2023

Cited by 17 | Viewed by 3321

Abstract

Road cracks are one of the external manifestations of safety hazards in transportation. At present, the detection and segmentation of road cracks is still an intensively researched issue. With the development of image segmentation technology of the convolutional neural network, the identification of road cracks has also ushered in new opportunities. However, the traditional road crack segmentation method has these three problems: 1. It is susceptible to the influence of complex background noise information. 2. Road cracks usually appear in irregular shapes, which increases the difficulty of model segmentation. 3. The cracks appear discontinuous in the segmentation results. Aiming at these problems, a network segmentation model of HC-Unet++ road crack detection is proposed in this paper. In this network model, a deep parallel feature fusion module is first proposed, one which can effectively detect various irregular shape cracks. Secondly, the SEnet attention mechanism is used to eliminate complex backgrounds to correctly extract crack information. Finally, the Blurpool pooling operation is used to replace the original maximum pooling in order to solve the crack discontinuity of the segmentation results. Through the comparison with some advanced network models, it is found that the HC-Unet++ network model is more precise for the segmentation of road cracks. The experimental results show that the method proposed in this paper has achieved 76.32% mIOU, 82.39% mPA, 85.51% mPrecision, 70.26% dice and Hd95 of 5.05 on the self-made 1040 road crack dataset. Compared with the advanced network model, the HC-Unet++ network model has stronger generalization ability and higher segmentation accuracy, which is more suitable for the segmentation detection of road cracks. Therefore, the HC-Unet++ network model proposed in this paper plays an important role in road maintenance and traffic safety. Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones)

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16 pages, 3584 KiB

Open AccessArticle

Improved Image Synthesis with Attention Mechanism for Virtual Scenes via UAV Imagery

by Lufeng Mo, Yanbin Zhu, Guoying Wang, Xiaomei Yi, Xiaoping Wu and Peng Wu

Drones 2023, 7(3), 160; https://doi.org/10.3390/drones7030160 - 25 Feb 2023

Cited by 4 | Viewed by 1916

Abstract

Benefiting from the development of unmanned aerial vehicles (UAVs), the types and number of datasets available for image synthesis have greatly increased. Based on such abundant datasets, many types of virtual scenes can be created and visualized using image synthesis technology before they are implemented in the real world, which can then be used in different applications. To achieve a convenient and fast image synthesis model, there are some common issues such as the blurred semantic information in the normalized layer and the local spatial information of the feature map used only in the generation of images. To solve such problems, an improved image synthesis model, SYGAN, is proposed in this paper, which imports a spatial adaptive normalization module (SPADE) and a sparse attention mechanism YLG on the basis of generative adversarial network (GAN). In the proposed model SYGAN, the utilization of the normalization module SPADE can improve the imaging quality by adjusting the normalization layer with spatially adaptively learned transformations, while the sparsified attention mechanism YLG improves the receptive field of the model and has less computational complexity which saves training time. The experimental results show that the Fréchet Inception Distance (FID) of SYGAN for natural scenes and street scenes are 22.1, 31.2; the Mean Intersection over Union (MIoU) for them are 56.6, 51.4; and the Pixel Accuracy (PA) for them are 86.1, 81.3, respectively. Compared with other models such as CRN, SIMS, pix2pixHD and GauGAN, the proposed image synthesis model SYGAN has better performance and improves computational efficiency. Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones)

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25 pages, 11167 KiB

Open AccessArticle

An Automatic Visual Inspection of Oil Tanks Exterior Surface Using Unmanned Aerial Vehicle with Image Processing and Cascading Fuzzy Logic Algorithms

by Mohammed A. H. Ali, Muhammad Baggash, Jaloliddin Rustamov, Rawad Abdulghafor, Najm Al-Deen N. Abdo, Mubarak H. G. Abdo, Talep S. Mohammed, Ameen A. Hasan, Ali N. Abdo, Sherzod Turaev and Yusoff Nukman

Drones 2023, 7(2), 133; https://doi.org/10.3390/drones7020133 - 13 Feb 2023

Cited by 6 | Viewed by 3155

Abstract

This paper presents an automatic visual inspection of exterior surface defects of oil tanks using unmanned aerial vehicles (UAVs) and image processing with two cascading fuzzy logic algorithms. Corrosion is one of the defects that has a serious effect on the safety of the surface of oil and gas tanks. At present, human inspection, and climbing robots inspection are the dominant approach for rust detection in oil and gas tanks. However, there are many shortcomings to this approach, such as taking longer, high cost, and covering less surface area inspection of the tank. The purpose of this research is to detect the rust in oil tanks by localizing visual inspection technology using UAVs, as well as to develop algorithms to distinguish between defects and noise. The study focuses on two basic aspects of oil tank inspection through the images captured by the UAV, namely, the detection of defects and the distinction between defects and noise. For the former, an image processing algorithm was developed to improve or remove noise, adjust the brightness of the captured image, and extract features to identify defects in oil tanks. Meanwhile, for the latter aspect, a cascading fuzzy logic algorithm and threshold algorithm were developed to distinguish between defects and noise levels and reduce their impact through three stages of processing: The first stage of fuzzy logic aims to distinguish between defects and low noise generated by the appearance of objects on the surface of the tank, such as trees or stairs, and reduce their impact. The second stage aims to distinguish between defects and medium noise generated by shadows or the presence of small objects on the surface of the tank and reduce their impact. The third stage of the thresholding algorithm aims to distinguish between defects and high noise generated by sedimentation on the surface of the tank and reduce its impact. The samples were classified based on the output of the third stage of the threshold process into defective or non-defective samples. The proposed algorithms were tested on 180 samples and the results show its superiority in the inspection and detection of defects with an accuracy of 83%. Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones)

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15 pages, 4363 KiB

Open AccessArticle

A Novel UAV Visual Positioning Algorithm Based on A-YOLOX

by Ying Xu, Dongsheng Zhong, Jianhong Zhou, Ziyi Jiang, Yikui Zhai and Zilu Ying

Drones 2022, 6(11), 362; https://doi.org/10.3390/drones6110362 - 18 Nov 2022

Cited by 5 | Viewed by 2860

Abstract

The application of UAVs is becoming increasingly extensive. However, high-precision autonomous landing is still a major industry difficulty. The current algorithm is not well-adapted to light changes, scale transformations, complex backgrounds, etc. To address the above difficulties, a deep learning method was here introduced into target detection and an attention mechanism was incorporated into YOLOX; thus, a UAV positioning algorithm called attention-based YOLOX (A-YOLOX) is proposed. Firstly, a novel visual positioning pattern was designed to facilitate the algorithm’s use for detection and localization; then, a UAV visual positioning database (UAV-VPD) was built through actual data collection and data augmentation and the A-YOLOX model detector developed; finally, corresponding high- and low-altitude visual positioning algorithms were designed for high- and low-altitude positioning logics. The experimental results in the actual environment showed that the AP50 of the proposed algorithm could reach 95.5%, the detection speed was 53.7 frames per second, and the actual landing error was within 5 cm, which meets the practical application requirements for automatic UAV landing. Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones)

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11 pages, 3852 KiB

Open AccessArticle

Inverse Airborne Optical Sectioning

by Rakesh John Amala Arokia Nathan, Indrajit Kurmi and Oliver Bimber

Drones 2022, 6(9), 231; https://doi.org/10.3390/drones6090231 - 2 Sep 2022

Cited by 4 | Viewed by 2889

Abstract

We present Inverse Airborne Optical Sectioning (IAOS), an optical analogy to Inverse Synthetic Aperture Radar (ISAR). Moving targets, such as walking people, that are heavily occluded by vegetation can be made visible and tracked with a stationary optical sensor (e.g., a hovering camera drone above forest). We introduce the principles of IAOS (i.e., inverse synthetic aperture imaging), explain how the signal of occluders can be further suppressed by filtering the Radon transform of the image integral, and present how targets’ motion parameters can be estimated manually and automatically. Finally, we show that while tracking occluded targets in conventional aerial images is infeasible, it becomes efficiently possible in integral images that result from IAOS. Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones)

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Review

Jump to: Editorial, Research

21 pages, 6631 KiB

Open AccessEditor’s ChoiceReview

An Overview of Drone Applications in the Construction Industry

by Hee-Wook Choi, Hyung-Jin Kim, Sung-Keun Kim and Wongi S. Na

Drones 2023, 7(8), 515; https://doi.org/10.3390/drones7080515 - 3 Aug 2023

Cited by 33 | Viewed by 32109

Abstract

The integration of drones in the construction industry has ushered in a new era of efficiency, accuracy, and safety throughout the various phases of construction projects. This paper presents a comprehensive overview of the applications of drones in the construction industry, focusing on their utilization in the design, construction, and maintenance phases. The differences between the three different types of drones are discussed at the beginning of the paper where the overview of the drone applications in construction industry is then described. Overall, the integration of drones in the construction industry has yielded transformative advancements across all phases of construction projects. As technology continues to advance, drones are expected to play an increasingly critical role in shaping the future of the construction industry. Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones)

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27 pages, 3381 KiB

Open AccessFeature PaperReview

A Comprehensive Survey of Transformers for Computer Vision

by Sonain Jamil, Md. Jalil Piran and Oh-Jin Kwon

Drones 2023, 7(5), 287; https://doi.org/10.3390/drones7050287 - 25 Apr 2023

Cited by 29 | Viewed by 8808

Abstract

As a special type of transformer, vision transformers (ViTs) can be used for various computer vision (CV) applications. Convolutional neural networks (CNNs) have several potential problems that can be resolved with ViTs. For image coding tasks such as compression, super-resolution, segmentation, and denoising, different variants of ViTs are used. In our survey, we determined the many CV applications to which ViTs are applicable. CV applications reviewed included image classification, object detection, image segmentation, image compression, image super-resolution, image denoising, anomaly detection, and drone imagery. We reviewed the state of the-art and compiled a list of available models and discussed the pros and cons of each model. Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones)

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