Phase-based motion analysis for high-precision measurement of bridge deflection using drone imagery

Mechanical Systems and Signal Processing, Journal Year: 2025, Volume and Issue: 228, P. 112433 - 112433

Published: Feb. 11, 2025

Language: Английский

---

YOLOv8-LDH: A lightweight model for detection of conveyor belt damage based on multispectral imaging

Measurement, Journal Year: 2025, Volume and Issue: 245, P. 116675 - 116675

Published: Jan. 5, 2025

Language: Английский

---

A Comprehensive Framework for Fine-Grained Object Recognition in Remote Sensing

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 131 - 150

Published: Jan. 1, 2025

Language: Английский

---

Integrating Drone in Agriculture: Addressing Technology, Challenges, Solutions, and Applications to Drive Economic Growth

Remote Sensing Applications Society and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 101576 - 101576

Published: May 1, 2025

Language: Английский

---

Edge Computing-Driven Real-Time Drone Detection Using YOLOv9 and NVIDIA Jetson Nano

Drones, Journal Year: 2024, Volume and Issue: 8(11), P. 680 - 680

Published: Nov. 19, 2024

Drones, with their ability to vertically take off and land stable hovering performance, are becoming favorable in both civilian military domains. However, this introduces risks of its misuse, which may include security threats airports, institutes national importance, VIP security, drug trafficking, privacy breaches, etc. To address these issues, automated drone detection systems essential for preventing unauthorized activities. Real-time requires high-performance devices such as GPUs. For our experiments, we utilized the NVIDIA Jetson Nano support YOLOv9-based detection. The performance evaluation YOLOv9 detect drones is based on metrics like mean average precision (mAP), frames per second (FPS), precision, recall, F1-score. Experimental data revealed significant improvements over previous models, a mAP 95.7%, 0.946, recall 0.864, an F1-score 0.903, marking 4.6% enhancement YOLOv8. This paper utilizes YOLOv9, optimized pre-trained weights transfer learning, achieving accuracy real-time Integrated Nano, system effectively identifies at altitudes ranging from 15 feet 110 while adapting various environmental conditions. model’s adaptability make it particularly suitable deployment security-sensitive areas, where quick accurate crucial. research establishes solid foundation future counter-drone applications shows great promise enhancing situational awareness critical, high-risk environments.

Language: Английский

---

Menumbuhkan Literasi Teknologi Melalui Pengenalan Aplikasi Computer Vision Di Kalangan Pelajar

Masyarakat Berkarya., Journal Year: 2024, Volume and Issue: 1(3), P. 51 - 60

Published: Aug. 5, 2024

The purpose of this community service project is to provide education enhance technological literacy by introducing the basics Computer Vision applications among students. activities included a seminar attended 170 An analysis using Wilcoxon statistical test on pre-post results showed significant improvement in participants' understanding applications. indicated difference before and after activity with value 0.011. Through service, participants have successfully grasped material presented as change agents digital era, positively impacting societal progress through improved technology field Vision.

Language: Английский

---

Phase-Based Motion Analysis for High-Precision Measurement of Bridge Deflection Using Drone Imagery

Published: Jan. 1, 2024

Language: Английский

---