(Universitas Negeri Malang, Indonesia)(2) Ferina Ayu Pusparani
(Universitas Negeri Malang, Indonesia)(3) Dyah Lestari
(Universitas Negeri Malang, Indonesia)(4) I Made Wirawan
(Universitas Negeri Malang, Indonesia)(5) Aji Prasetya Wibawa
(Universitas Negeri Malang, Indonesia)(6) Osamu Fukuda
(Saga University, Japan)*corresponding author
AbstractThe rapid advancement and increasing demand for Unmanned Surface Vehicle (USV) technology have drawn considerable attention in various sectors, including commercial, research, and military, particularly in marine and shallow water applications. USVs have the potential to revolutionize monitoring systems in remote areas while reducing labor costs. One critical requirement for USVs is their ability to autonomously integrate Guidance, Navigation, and Control (GNC) technology, enabling self-reliant operation without constant human oversight. However, current study for USV shown the use of traditional method using color detection which is inadequate to detect object with unstable lighting condition. This study addresses the challenge of enabling Autonomous Surface Vehicles (ASVs) to operate with minimal human intervention by enhancing their object detection and classification capabilities. In dynamic environments, such as water surfaces, accurate and rapid object recognition is essential. To achieve this, we focus on the implementation of deep learning algorithms, including the YOLO algorithm, to empower USVs with informed navigation decision-making capabilities. Our research contributes to the field of robotics by designing an affordable USV prototype capable of independent operation characterized by precise object detection and classification. By bridging the gap between advanced visualization techniques and autonomous USV technology, we envision practical applications in remote monitoring and marine operations with object detection. This paper presents the initial phase of our research, emphasizing significance of deep learning algorithms for enhancing USV navigation and decision-making in dynamic environmental conditions, resulting in mAP of 99.51%, IoU of 87.80%, error value of the YOLOv4-tiny image processing algorithm is 0.1542. KeywordsReal-Time; Onstacle Detection; Unmanned Surface Vehicle; Maneuver
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DOIhttps://doi.org/10.31763/ijrcs.v3i4.1147 |
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International Journal of Robotics and Control Systems
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