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All Journal Journal of Applied Science, Engineering and Technology (J. ASET)
Ummah , Khairul
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Intelligent Eyes on the Battlefield: Developing an AI-Vision Based Military Vehicle and Infantry Detection System Wibowo, Pasha R A; Ummah , Khairul; Arifianto, Ony; Widagdo, Djarot; Riszal, Akhmad; Arif, Yanuar Zulardiansyah
Journal of Applied Science, Engineering and Technology Vol. 3 No. 2 (2023): December 2023
Publisher : INSTEP Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/jaset.v3i2.63

Abstract

The importance of accurate, real-time intelligence in modern warfare is crucial, especially in reconnaissance and surveillance operations. Currently, drones are widely used for reconnaissance, but generally rely only on the operator's ability to monitor operation targets. This research is aimed at developing an AI vision assistance system to enhance the ability to detect military vehicles and infantry. The method used is computer vision trained to recognize and differentiate several military objects. The YOLO model is used to detect and distinguish objects. To improve detection capabilities, the YOLO v8 model was retrained with an additional dataset sourced from battle recordings on the battlefield. The results show a detection accuracy rate of 95% in detecting vehicles and infantry under normal visual conditions. The model from this research can be used to enhance the capabilities of reconnaissance drones and the effectiveness of monitoring operations.
Aircraft Detection in Low Visibility Condition Using Artificial Intelligence Sembiring, Javensius; Ummah , Khairul; Widyosekti, M. Dhiku; Arif, Yanuar Zulardiansyah; Huda, Zulmiftah
Journal of Applied Science, Engineering and Technology Vol. 4 No. 1 (2024): June 2024
Publisher : INSTEP Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/jaset.v4i1.64

Abstract

Bad weather often interferes with the functioning of the air transport system. One example is the frequent flight delays for commercial aircraft, resulting in losses for both the airline and passengers. Artificial Intelligence (AI) technology can now minimize delays caused by bad weather, especially in low visibility conditions. This paper discusses AI modeling that can detect aircraft in a low visibility weather condition, especially in the airport area. The employed method is the deep learning approach with the YOLOv4 algorithm (single-stage detection), which is regarded as one of the optimal platforms in this field. There are 600 images used in this work to create and train three different models. Image Dehazing filter is employed on the training data before it is trained to produce the detection model. The result shows that the model has a good performance in terms of performance metrices. Thus, this model is suitable to be used to detect aircraft in low visibility conditions.
Co-Authors Arif, Yanuar Zulardiansyah Arifianto, Ony DJAROT WIDAGDO, DJAROT Huda, Zulmiftah Javensius Sembiring, Javensius Riszal, Akhmad Wibowo, Pasha R A Widyosekti, M. Dhiku