https://publications.eai.eu/index.php/inis <p>EAI Endorsed Transactions on Industrial Networks and Intelligent Systems is open access, a peer-reviewed scholarly journal focused on ubiquitous computing, cloud computing, and cyber-physical system, all kinds of networks in large-scale factories, including a lot of traditional and new industries. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). <strong>This journal is co-organized, and managed by Duy Tan University, Vietnam</strong> <strong>in collaboration with Passage to ASEAN (P2A), an ASEAN Entity</strong>.</p> <p><strong>INDEXING</strong>: Scopus (CiteScore: 3.1), Compendex, DOAJ, ProQuest, EBSCO, DBLP</p> European Alliance for Innovation (EAI) en-US EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2410-0218 <p>This is an open-access article distributed under the terms of the Creative Commons Attribution <a href="https://creativecommons.org/licenses/by/3.0/" target="_blank" rel="noopener">CC BY 3.0</a> license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.</p> https://publications.eai.eu/index.php/inis/article/view/8279 <div class="page" title="Page 1"><div class="section"><div class="layoutArea"><div class="column"><p>Detecting aircraft in satellite images poses considerable challenges due to complex backgrounds and variable conditions influenced by sensor geometry and atmospheric factors. Despite rapid advancements in deep learning algorithms, their main focus has been on ground-based imagery. This study offers a thorough evaluation and comparison of advanced object detection algorithms specifically designed for aircraft detection in satellite imagery. By leveraging the extensive HRPlanesV2 dataset and a rigorous validation process on the GDIT dataset, we trained a cutting-edge object detection model, YOLO-Mamba, published in June 2024. Additionally, we introduce YOLO-Mamba-TransGhost, which integrates a novel Transformer module SC3T and Ghost Convolution into the YOLO model’s backbone architecture. Furthermore, substituting the WIoU loss function with CIoU in YOLO-Mamba results in significant improvements in accuracy and small object detection. Experimental results on the GDIT dataset indicate that YOLO-Mamba-TransGhost improves mAP@.5 by approximately 2% compared to the original YOLO-Mamba. Similarly, tests on the HRPlanev2 data set reveal a notable reduction in model complexity and an impressive accuracy of 98.7% which is achieved by leveraging a cost-sensitive loss function that dynamically focuses training on higher quality samples, improving convergence and accuracy. Therefore, the proposed YOLO-Mamba-TransGhost model demonstrates superior accuracy and reduced complexity in aircraft detection from satellite imagery, highlighting its potential for practical applications in aerospace monitoring, disaster management, and surveillance systems domain.</p></div></div></div></div> Manh-Tuan Do Manh-Hung Ha Minh-Huy Le Oscal Tzyh-Chiang Copyright (c) 2025 Manh-Tuan Do, Manh-Hung Ha, Minh-Huy Le, Oscal Tzyh-Chiang https://creativecommons.org/licenses/by-nc-sa/4.0 2025-09-22 2025-09-22 12 4 10.4108/eetinis.v12i4.8279