Optical multi-band detection of unmanned aerial vehicles with YOLO v4 convolutional neural network

Golyak, Igor S.; Anfimov, Dmitriy R.; Fufurin, I. L.; Nazolin, A. L.; Bashkin, S. V.; Glushkov, Vladimir L.; Morozov, A.

doi:10.1117/12.2584591

Cited by 3 publications

(1 citation statement)

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“…e Yolo algorithm treats object detection as a regression problem and predicts bounding box coordinates and class probabilities directly from the full image. In recent years, the Yolo v2 algorithm has improved in the prediction accuracy, identifying more objects, and speed [27]. e Yolo v3 algorithm has changed the size of the model structure to measure the speed and accuracy of detection, and improved the detection range through multiple downsampling layers, and then improved the detection accuracy.…”

Section: Yolo V4mentioning

confidence: 99%

Combining Self-Supervised Learning and Yolo v4 Network for Construction Vehicle Detection

Zhang

Hou

2022

Mobile Information Systems

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At present, there are many application fields of target detection, but it is very difficult to apply intelligent traffic target detection in the construction site because of the complex environment and many kinds of engineering vehicles. A method based on self-supervised learning combined with the Yolo (you only look once) v4 network defined as “SSL-Yolo v4” (self-supervised learning-Yolo v4) is proposed for the detection of construction vehicles. Based on the combination of self-supervised learning network and Yolo v4 algorithm network, a self-supervised learning method based on context rotation is introduced. By using this method, the problem that a large number of manual data annotations are needed in the training of existing deep learning algorithms is solved. Furthermore, the self-supervised learning network after training is combined with Yolo v4 network to improve the prediction ability, robustness, and detection accuracy of the model. The performance of the proposed model is optimized by performing five-fold cross validation on the self-built dataset, and the effectiveness of the algorithm is verified. The simulation results show that the average detection accuracy of the SSL-Yolo v4 method combined with self-supervised learning is 92.91%, 4.83% detection speed is improved, 7–8 fps detection speed is improved, and 8–9% recall rate is improved. The results show that the method has higher precision and speed and improves the ability of target prediction and the robustness of engineering vehicle detection.

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Section: Yolo V4mentioning

confidence: 99%

Combining Self-Supervised Learning and Yolo v4 Network for Construction Vehicle Detection

Zhang

Hou

2022

Mobile Information Systems

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Drone-type-Set: Drone types detection benchmark for drone detection and tracking

AlDosari,

Osman,

Elharrouss

et al. 2024

2024 International Conference on Intelligent Systems and Computer Vision (ISCV)

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YOLO-Based UAV Technology: A Review of the Research and Its Applications

Chen

Zheng

et al. 2023

Drones

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In recent decades, scientific and technological developments have continued to increase in speed, with researchers focusing not only on the innovation of single technologies but also on the cross-fertilization of multidisciplinary technologies. Unmanned aerial vehicle (UAV) technology has seen great progress in many aspects, such as geometric structure, flight characteristics, and navigation control. The You Only Look Once (YOLO) algorithm was developed and has been refined over the years to provide satisfactory performance for the real-time detection and classification of multiple targets. In the context of technology cross-fusion becoming a new focus, researchers have proposed YOLO-based UAV technology (YBUT) by integrating the above two technologies. This proposed integration succeeds in strengthening the application of emerging technologies and expanding the idea of the development of YOLO algorithms and drone technology. Therefore, this paper presents the development history of YBUT with reviews of the practical applications of YBUT in engineering, transportation, agriculture, automation, and other fields. The aim is to help new users to quickly understand YBUT and to help researchers, consumers, and stakeholders to quickly understand the research progress of the technology. The future of YBUT is also discussed to help explore the application of this technology in new areas.

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Optical multi-band detection of unmanned aerial vehicles with YOLO v4 convolutional neural network

Cited by 3 publications

References 0 publications

Combining Self-Supervised Learning and Yolo v4 Network for Construction Vehicle Detection

Combining Self-Supervised Learning and Yolo v4 Network for Construction Vehicle Detection

Drone-type-Set: Drone types detection benchmark for drone detection and tracking

YOLO-Based UAV Technology: A Review of the Research and Its Applications

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