YOLOv3-Based Matching Approach for Roof Region Detection from Drone Images

Yeh, Chia Cheng; Chang, Yang Lang; Alkhaleefah, Mohammad; Hsu, Pai-Hui; Eng, Weiyong; Koo, Voon Chet; Huang, Bormin; Chang, Li-Chiu

doi:10.3390/rs13010127

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…After refining the results through filtering techniques [162], the UAV's path control can be adjusted accordingly [163]. In this context, regression-based approaches aim to compute the object's correlative and class probability, including YOLO [164], YOLOv2 [165], YOLOv3 [166], YOLOv4 [167], and YOLOv5 [168]. On one hand, YOLO aims to detect small objects in real time by identification in image frames.…”

Section: Target Trackingmentioning

confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

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Section: Target Trackingmentioning

confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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“…With the development of computing power, neural networks are also used in image matching. Yeh C C [4] proposed YOLOv3 combined with traditional feature point matching algorithm in 2021, whose descriptive features are extracted from the UAVs residential dataset for roof detection. YOLOv3 only performs feature extraction on the proposed candidate regions, thus greatly reducing the complexity of image matching.…”

Section: Introductionmentioning

confidence: 99%

Self-localization of UAVs based on visual-inertial integrated navigation

Zheng,

Lei,

Tan

2024

Advanced Fiber Laser Conference (AFL2023)

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With the increasing application of unmanned aerial vehicles (UAVs) in all walks of life, the demand for autonomous navigation of UAVs has become increasingly urgent, especially in areas where there is no GPS signal or where the GPS signal is interfered with. This paper proposes an integrated navigation method based on visual navigation and inertial navigation. First, it leverages the satellite map as the reference map. During the flight of the UAV, the camera takes pictures of the ground at intervals. And then it matches the photo of the UAV and the reference map. In this way, the visual navigation system localizes the UAV and evalutate the the reliability of the localization. Finally the Particle Filter algorithm is introduced to fuse the positioning results. To expedite the matching process, we utilize the INS to narrow down the range of satellite maps for matching. Considering the differences between the camera photo and the reference map, this paper introduces SuperPoint and SuperGlue algorithms for feature extraction and matching, respectively. These two algorithms utilize deep neural networks to extract and match the features of the images, enabling the extraction of deep semantic features rather than manual features. Experiment results demonstrate the superior matching effectiveness of the image matching algorithm introduced in this paper. The simulation results show that the cumulative error of INV is greatly reduced after fusing with the results of visual navigation. Since it navigates autonomously, the integrated navigation method offers robust anti-interference capabilities, high autonomy, and better adaptability.

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“…The algorithm based on the ORB feature is superior to several other popular algorithms in terms of speed while ensuring the feature points have rotation and scale invariance. The UAV image has the characteristics of a large amount of data, so the speed of the image processing algorithm is very strict [5]. Considering the processing speed, this paper selects the ORB algorithm to extract the feature points of the aerial image.…”

Section: Introductionmentioning

confidence: 99%

UAV Low-Altitude Aerial Image Stitching Based on Semantic Segmentation and ORB Algorithm for Urban Traffic

et al. 2022

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UAVs are flexible in action, changeable in shooting angles, and complex and changeable in the shooting environment. Most of the existing stitching algorithms are suitable for images collected by UAVs in static environments, but the images are in fact being captured dynamically, especially in low-altitude flights. Considering that the great changes of the object position may cause the low-altitude aerial images to be affected by the moving foreground during stitching, so as to result in quality problems, such as splicing misalignment and tearing, a UAV aerial image stitching algorithm is proposed based on semantic segmentation and ORB. In the image registration, the algorithm introduces a semantic segmentation network to separate the foreground and background of the image and obtains the foreground semantic information. At the same time, it uses the quadtree decomposition idea and the classical ORB algorithm to extract feature points. By comparing the feature point information with the foreground semantic information, the foreground feature points can be deleted to realize feature point matching. Based on the accurate image registration, the image stitching and fusion will be achieved by the homography matrix and the weighted fusion algorithm. The proposed algorithm not only preserves the details of the original image, but also improves the four objective data points of information entropy, average gradient, peak signal-to-noise ratio and root mean square error. It can solve the problem of splicing misalignment tearing during background stitching caused by dynamic foreground and improves the stitching quality of UAV low-altitude aerial images.

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YOLOv3-Based Matching Approach for Roof Region Detection from Drone Images

Cited by 10 publications

References 37 publications

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Self-localization of UAVs based on visual-inertial integrated navigation

UAV Low-Altitude Aerial Image Stitching Based on Semantic Segmentation and ORB Algorithm for Urban Traffic

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