Bulletin of Intelligent Machines and Algorithms
BIMA (Bulletin of Intelligent Machines and Algorithms) is an international peer-reviewed journal dedicated to promoting research in the fields of artificial intelligence, machine learning, and algorithms. BIMA serves as a platform for publishing the latest research findings and innovative applications in these rapidly evolving fields. The journal aims to contribute to the academic and professional development of researchers, practitioners, and educators by publishing high-quality articles that provide in-depth insights into the theoretical, practical, and computational aspects of intelligent systems and algorithms. Focus and Scope BIMA publishes original research articles, reviews, and technical reviews on various topics related to intelligent machines and algorithms. The scope of this journal includes, but is not limited to: Artificial Intelligence: Methodologies, algorithms, and architectures for building intelligent systems, including knowledge representation, reasoning, learning, and perception. Machine Learning: Supervised, unsupervised, semi-supervised, and reinforcement learning algorithms; applications in real-world problems. Deep Learning: Advanced neural network architectures such as CNNs, RNNs, Transformers, and their applications in various domains including image, video, text, and signal processing. Computer Vision: Image processing, object detection and recognition, image segmentation, motion analysis, and visual scene understanding in intelligent systems. Data Mining: Techniques for extracting patterns and knowledge from large datasets. Optimisation Algorithms: Theory and applications of optimisation techniques in continuous and discrete domains. Computational Intelligence: Evolutionary algorithms, fuzzy logic, and swarm intelligence systems. Natural Language Processing (NLP): Advances in language understanding, translation, and text analysis. Applications: Applications of artificial intelligence and algorithms in healthcare, finance, industry, education, and other fields. Robotics and Autonomous Systems: Intelligent robots, human-robot interaction, and autonomous vehicles.
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<![CDATA[A Progressive Training Framework for Robust YOLOv11-Based Vehicle Detection Across Domain Shifts in Real-World CCTV Environments ]]>
<![CDATA[Aditya Gunaldhi]]>;
<![CDATA[Budiman]]>;
<![CDATA[Chairul Habibi]]>;
<![CDATA[Nur Alamsyah]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 5 (2026): BIMA July 2026 Issue ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i5.27
<![CDATA[The performance of deep learning-based vehicle detection models often deteriorates when applied to real-world CCTV environments due to domain shift caused by variations in lighting, occlusion, glare, and changes in camera viewpoint. This study aims to develop a YOLOv11-based progressive training framework to improve the model’s generalization ability under heterogeneous operational conditions. The proposed method consists of four stages: base training, fine-tuning, and a supervised progressive domain adaptation strategy implemented through sequential fine-tuning on labeled target-domain CCTV images before OpenVINO-based inference optimization. The model is trained using a source dataset and adapted to a target dataset representing real-world CCTV conditions. Evaluation was conducted using Precision, Recall, mAP@50, mAP@50–95, loss curve analysis, per-vehicle-class evaluation, visual testing on CCTV video, as well as latency and throughput measurements. The results show that base training achieved an mAP@50 of 0.919 and built a robust feature representation, while fine-tuning maintained performance stability with an mAP@50 of 0.915. Although domain adaptation reduced mAP@50 to 0.811 due to domain shift, the model demonstrated improved generalization capabilities and maintained more consistent detection under low-light conditions, glare, occlusion, and heavy traffic. OpenVINO INT8 optimization increased inference speed from 4.86 FPS to 9.08 FPS with minimal accuracy loss. These findings demonstrate that the progressive training framework effectively bridges differences in data distribution while producing a vehicle detection model that is more robust, efficient, and suitable for deployment in real-time CCTV-based traffic monitoring systems.]]>
