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All Journal Insyst : Journal of Intelligent System and Computation The Indonesian Journal of Computer Science
Sutanto, Richard
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Fire and Smoke Detection with 3D Position Estimation Using YOLO Dinata, Yuwono; Anthony, Bryan; Sutanto, Richard
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4541

Abstract

Fire and smoke detection systems are essential for safety, but traditional methods often face issues like false alarms and poor localization accuracy. This study integrates advanced object detection models, a confidence-based voting mechanism, and 3D localization to address these challenges. Using three cameras, the system detects fire or smoke and estimates its 3D position (x, y, z) through bounding box depth estimation and camera placement. A voting mechanism enhances reliability by requiring validation from at least two cameras with a confidence threshold of 0.5. YOLOv5s achieved 92% accuracy, 96% precision, 95% recall, mAP50 of 98%, and 87.02 FPS, making it suitable for real-time use. YOLOFM-NADH+C3 offered comparable accuracy (92%) but better localization precision with a 0.22 cm error versus YOLOv5s’ 1.53 cm, albeit at a slower FPS (54.82). Experiments confirm the system’s ability to reduce false positives and localize fire/smoke accurately under challenging conditions.
Deep Learning Models Comparison for Emotion Classification With Image Pre-Processing Methods Anthony, Bryan; Lienardi, Nicholas; Sutanto, Richard; Dinata, Yuwono
Intelligent System and Computation Vol 6 No 2 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i2.398

Abstract

This research investigates advancements in Facial Expression Recognition (FER) within the domain of affective computing, focusing on improving the accuracy and robustness of FER systems under diverse, real-world conditions. Facial expressions serve as critical non-verbal cues in human communication, yet existing FER systems often face challenges due to environmental variability such as changes in lighting, pose, and occlusions. This study evaluates the performance of three Convolutional Neural Network (CNN) architectures—ResNet50, VGG16, and MobileNetV3Large—integrated with preprocessing techniques like Contrast Limited Adaptive Histogram Equalization (CLAHE) and the Synthetic Minority Oversampling Technique (SMOTE). These methods address key challenges such as class imbalance and low contrast in datasets. Results demonstrate the pivotal role of tailored preprocessing strategies. For instance, the application of CLAHE and SMOTE improved the VGG16 model's test accuracy from 0.70 to 0.79, representing a 0.09 or 9% increase. This significant improvement underscores the effectiveness of combining advanced preprocessing methods with CNN architectures. Furthermore, the findings highlight the advantages of optimizing preprocessing to enhance the recognition of subtle emotions in uncontrolled settings, offering practical insights for deploying FER systems in real-time applications. Overall, this research demonstrates the potential of preprocessing techniques to enhance FER system performance significantly, particularly when paired with well-established deep learning models. These insights pave the way for the development of more accurate, robust, and adaptable FER systems capable of functioning reliably in dynamic, real-world environments.
Co-Authors Anthony, Bryan Dinata, Yuwono Lienardi, Nicholas