The Detection System of Helipad for Unmanned Aerial Vehicle Landing Using YOLO Algorithm

Suprapto, Bhakti Yudho; Wahyudin, Agus; Hikmarika, Hera; Dwijayanti, Suci

doi:10.26555/jiteki.v7i2.20684

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…This can involve the use of CNN algorithms [102] or deep learning [103] to help extract the necessary information needed for object identification. Currently, many ready-made algorithms [104], [105] are available for image processing, which can be used to reduce design time for image analysis. The image classification system utilized in this study is based on convolutional neural network (CNN) and its architecture is presented in Fig.…”

Section: A Architecture and Environment Of The Systemmentioning

confidence: 99%

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

Chotikunnan,

Puttasakul,

Chotikunnan

et al. 2023

Journal of Robotics and Control

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This study presents a method for enhancing the accuracy of object detection in industrial automation applications using ResNet18-based image segmentation. The objective is to extract object images from the background image accurately and efficiently. The study includes three experiments, RGB to grayscale conversion, single image processing, and dual image processing. The results of the experiments show that dual image processing is superior to both RGB to grayscale conversion and single image processing techniques in accurately identifying object edges, determining CG values, and cutting background images and gripper heads. The program achieved a 100% success rate for objects located in the workpiece tray, while also identifying the color and shape of the object using ResNet-18. However, single image processing may have advantages in certain scenarios with sufficient image information and favorable lighting conditions. Both methods have limitations, and future research could focus on further improvements and optimization of these methods, including separating objects into boxes of each type and converting image coordinate data into robot working area coordinates. Overall, this study provides valuable insights into the strengths and limitations of different object recognition techniques for industrial automation applications.

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Section: A Architecture and Environment Of The Systemmentioning

confidence: 99%

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

Chotikunnan,

Puttasakul,

Chotikunnan

et al. 2023

Journal of Robotics and Control

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show abstract

“…The region convolutional neural network's predictions differ from those of the synthetic neural network (R-CNN). The system needs thousands of predictions to process an image, which makes YOLO considerably quicker than R-CNN [19]. Using all of the image's attributes, the YOLO neural network immediately extracts candidate boxes from images to identify items [20].…”

Section: You Only Look Once (Yolo)mentioning

confidence: 99%

A New Surveillance and Security Alert System Based on Real-Time Motion Detection

AL-SLEMANİ¹,

ZENGİN²

2023

Journal of Smart Systems Research

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Due to the frequent and widespread instances of burglary, effective and accurate intrusion detection with an alarm system is now necessary. In this study, the proposed system is used for secret and vital places that need a good monitoring system based on human motion detection. The system can protect museums that contain critical historical objects and libraries with old historical books. The security alert system is also essential to the protection of banks. The system uses a two-frame difference algorithm and a simple background modeling algorithm for motion detection with the help of AForge.NET. Whenever the system detects motion, it interprets the frames of motion to determine humans by using YOLO. The security alert system activates the alerts whenever it detects human motion, like sending an email to an authorized person, triggering the LED alert and sound alert, recording a video of that place, taking a motion picture, and saving it within the databases. The system has the advantages of low cost, a user-friendly interface, and high security. The system was thoroughly tested to detect human movement in unauthorized areas. The system successfully detected human activity indoors and outdoors and activated the alarms. Also, the system could detect human movement in all four directions and alert observers directly at the time of the incident. In low light or minimum illumination, the system can determine human motion. The evaluation results showed that the system's accuracy in detecting human movement was 95%, which shows that the system is able to protect vital and secret places and can successfully monitor areas.

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License Plate Localization and Character Recognition using YOLOv8 and Convolutional Neural Network

Nadif,

Kusrini

2023

2023 Eighth International Conference on Informatics and Computing (ICIC)

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The Detection System of Helipad for Unmanned Aerial Vehicle Landing Using YOLO Algorithm

Cited by 6 publications

References 14 publications

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

A New Surveillance and Security Alert System Based on Real-Time Motion Detection

License Plate Localization and Character Recognition using YOLOv8 and Convolutional Neural Network

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