scholarly journals Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3958
Author(s):  
Seongkyun Han ◽  
Jisang Yoo ◽  
Soonchul Kwon
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Detection Method ◽  
Vehicle Detection ◽  
Research Area ◽  
Angular Distortion ◽  
Background Information ◽  
Feature Maps ◽  
Detection Model ◽  
Aerial Vehicle

Vehicle detection is an important research area that provides background information for the diversity of unmanned-aerial-vehicle (UAV) applications. In this paper, we propose a vehicle-detection method using a convolutional-neural-network (CNN)-based object detector. We design our method, DRFBNet300, with a Deeper Receptive Field Block (DRFB) module that enhances the expressiveness of feature maps to detect small objects in the UAV imagery. We also propose the UAV-cars dataset that includes the composition and angular distortion of vehicles in UAV imagery to train our DRFBNet300. Lastly, we propose a Split Image Processing (SIP) method to improve the accuracy of the detection model. Our DRFBNet300 achieves 21 mAP with 45 FPS in the MS COCO metric, which is the highest score compared to other lightweight single-stage methods running in real time. In addition, DRFBNet300, trained on the UAV-cars dataset, obtains the highest AP score at altitudes of 20–50 m. The gap of accuracy improvement by applying the SIP method became larger when the altitude increases. The DRFBNet300 trained on the UAV-cars dataset with SIP method operates at 33 FPS, enabling real-time vehicle detection.

scholarly journals Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

2020 ◽  
Vol 12 (1) ◽  
pp. 182 ◽  
Author(s):  
Lingxuan Meng ◽  
Zhixing Peng ◽  
Ji Zhou ◽  
Jirong Zhang ◽  
Zhenyu Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Detection System ◽  
Detection Model ◽  
Aerial Vehicle ◽  
Real Time Detection ◽  
Pipeline Safety

Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.

scholarly journals YOLO-Highway: An Improved Highway Center Marking Detection Model for Unmanned Aerial Vehicle Autonomous Flight

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhiwei Zhao ◽  
Jianfeng Han ◽  
Lili Song
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Visual Navigation ◽  
Detection Accuracy ◽  
Highway Maintenance ◽  
The Real ◽  
Detection Model ◽  
Proposed Model ◽  
Time Requirements ◽  
Aerial Vehicle

Automatic visual navigation flight of an unmanned aerial vehicle (UAV) plays an important role in the highway maintenance field. Automatic highway center marking detection is the most important part of the visual navigation flight of a UAV. In this study, the UAV-viewed highway data are collected from the UAV perspective. This paper proposes a model named the YOLO-Highway that uses an improved form of the You Only Look Once (YOLO) model to enhance the real-time detection of highway marking problems. The proposed model is mainly designed by optimizing the network structure and the loss function of the original YOLOv3 model. The proposed model is verified by the experiments using the highway center marking dataset, and the results show that the average precision (AP) of the trained model is 82.79%, and the frames per second (FPS) is 25.71 f/s. In comparison with the original YOLOv3 model, the detection accuracy of the proposed model is improved by 7.05%, and its speed is improved by 5.29 f/s. Moreover, the proposed model had stronger environmental adaptability and better detection precision and speed than the original model in complex highway scenarios. The experimental results show that the proposed YOLO-Highway model can accurately detect the highway center markings in real-time and has high robustness to changes in different environmental conditions. Therefore, the YOLO-Highway model can meet the real-time requirements of the highway center marking detection.

scholarly journals A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5

2021 ◽  
Vol 13 (9) ◽  
pp. 1619
Author(s):  
Bin Yan ◽  
Pan Fan ◽  
Xiaoyan Lei ◽  
Zhijie Liu ◽  
Fuzeng Yang
Keyword(s):  
Real Time ◽  
Apple Tree ◽  
Detection Method ◽  
Recognition Algorithm ◽  
Medium Size ◽  
Feature Maps ◽  
Detection Algorithms ◽  
Fusion Mode ◽  
Visual Attention Mechanism ◽  
Tree Image

The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

scholarly journals Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4115 ◽  
Author(s):  
Yuxia Li ◽  
Bo Peng ◽  
Lei He ◽  
Kunlong Fan ◽  
Zhenxu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Computational Efficiency ◽  
Neural Nets ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aerial Vehicle

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.

scholarly journals Pemanfaatan Metode Fotogrametri untuk Pengukuran Garis Pantai dan Identifikasi Objek-Objek Tematik dengan Menggunakan Wahana Uav (Unmanned Aerial Vehicle) (Studi Kasus Pengukuran Garis Pantai di Pangkalan TNI AL Pondok Dayung

2020 ◽  
Vol 5 (1) ◽  
pp. 71-84
Author(s):  
Adhyta Harfan ◽  
Dipo Yudhatama ◽  
Imam Bachrodin
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Control Point ◽  
Ground Control ◽  
Ground Control Point ◽  
Real Time Kinematic ◽  
Aerial Vehicle

Metode Fotogrametri telah banyak digunakan dalam survei dan pemetaan. Seiring dengan kemajuan ilmu pengetahuan dan teknologi, metode fotogrametri saat ini berbasiskan pesawat tanpa awak atau yang lebih dikenal dengan UAV (Unmanned Aerial Vehicle). Kelebihan metode fotogrametri berbasiskan UAV untuk pengukuran garis pantai adalah memiliki resolusi spasial yang sangat tinggi dan dapat menjagkau daerah-daerah yang sulit dan berbahaya. Di samping itu juga dapat memberikan data foto udara terkini dengan sekala detail. Dalam penelitian ini membandingkan ketelitian horisontal antara hasil pengukuran garis pantai menggunakan metode fotogrametri berbasiskan UAV secara rektifikasi dengan GCP (Ground Control Point) maupun secara PPK (Post Processed Kinematic) dengan pengukuran garis pantai metode GNSS RTK (Real Time Kinematic). Hasil perhitungan ketelitian horisontal mengacu pada standar publikasi IHO S-44 tentang pengukuran garis pantai. Pemotretan dilakukan dengan ketinggian terbang 180 m, dengan tampalan depan dan samping 80%. Hasil perhitungan ketelitian horisontal foto udara terektifikasi 5 GCP, foto udara PPK dan foto udara PPK terektifikasi 1 GCP terhadap pengukuran garis pantai dengan metode GNSS RTK diperoleh nilai standar deviasi (σ) dan 95% selang kepercayaan (CI95%) masing-masing sebagai berikut: σ5gcp=10,989 cm dengan CI95% 16.8 cm < μ < 21.2 cm , σppk=26,066 cm dengan CI95% 26.5 cm < μ < 37 cm dan σppk1gcp=10,378 cm dengan CI95% 15.6 cm < μ < 19.8 cm. Kemudian terdapat 10 objek tematik berdasarkan Peta Laut Nomor 1 yang dapat diinterpretasi pada hasil orthomosaic foto udara.

scholarly journals Utilizing drone technology in the civil engineering

2019 ◽  
Vol 14 (1) ◽  
pp. 27-37
Author(s):  
Matúš Tkáč ◽  
Peter Mésároš
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Civil Engineering ◽  
Aerial Images ◽  
General Overview ◽  
Aerial Photogrammetry ◽  
Different Types ◽  
Aerial Vehicle

Abstract An unmanned aerial vehicle (UAVs), also known as drone technology, is used for different types of application in the civil engineering. Drones as a tools that increase communication between construction participants, improves site safety, uses topographic measurements of large areas, with using principles of aerial photogrammetry is possible to create buildings aerial surveying, bridges, roads, highways, saves project time and costs, etc. The use of UAVs in the civil engineering can brings many benefits; creating real-time aerial images from the building objects, overviews reveal assets and challenges, as well as the broad lay of the land, operators can share the imaging with personnel on site, in headquarters and with sub-contractors, planners can meet virtually to discuss project timing, equipment needs and challenges presented by the terrain. The aim of this contribution is to create a general overview of the use of UAVs in the civil engineering. The contribution also contains types of UAVs used for construction purposes, their advantages and also disadvantages.

scholarly journals Using Improved Edge Detection Method to Detect Mining-Induced Ground Fissures Identified by Unmanned Aerial Vehicle Remote Sensing

2021 ◽  
Vol 13 (18) ◽  
pp. 3652
Author(s):  
Duo Xu ◽  
Yixin Zhao ◽  
Yaodong Jiang ◽  
Cun Zhang ◽  
Bo Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Unmanned Aerial Vehicle ◽  
Field Experiments ◽  
High Efficiency ◽  
Detection Method ◽  
Laplacian Operator ◽  
Ground Fissures ◽  
Aerial Vehicle ◽  
Edge Detection Method

Information on the ground fissures induced by coal mining is important to the safety of coal mine production and the management of environment in the mining area. In order to identify these fissures timely and accurately, a new method was proposed in the present paper, which is based on an unmanned aerial vehicle (UAV) equipped with a visible light camera and an infrared camera. According to such equipment, edge detection technology was used to detect mining-induced ground fissures. Field experiments show high efficiency of the UAV in monitoring the mining-induced ground fissures. Furthermore, a reasonable time period between 3:00 a.m. and 5:00 a.m. under the studied conditions helps UAV infrared remote sensing identify fissures preferably. The Roberts operator, Sobel operator, Prewitt operator, Canny operator and Laplacian operator were tested to detect the fissures in the visible image, infrared image and fused image. An improved edge detection method was proposed which based on the Laplacian of Gaussian, Canny and mathematical morphology operators. The peak signal-to-noise rate, effective edge rate, Pratt’s figure of merit and F-measure indicated that the proposed method was superior to the other methods. In addition, the fissures in infrared images at different times can be accurately detected by the proposed method except at 7:00 a.m., 1:00 p.m. and 3:00 p.m.

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