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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Emerging Science Journal Indonesian Journal of Electrical Engineering and Computer Science
Hossen, Md. Jakir
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Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Environmental Science

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Real-time object detection and XAI-based activation map visualization using YOLOv8s Yusof, Ashaari; Hishamuddin, Muhammad; Hossen, Md. Jakir
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9765

Abstract

This study introduced a methodology for real-time object detection and interpretability using YOLOv8s, trained on the MS common objects in context (COCO) dataset. The system captured live webcam footage, processes frames resized to 640×384, and applies YOLOv8s to detect objects with bounding boxes, labels, and confidence scores. YOLOv8s architecture comprising a CSPDarknet53-based backbone, neck, and head ensures efficient feature extraction and accurate detection. To enhance model transparency, activation map generation is implemented by attaching forward hooks to intermediate convolutional layers. Feature maps are captured during the forward pass, averaged, normalized, and resized to match the original image dimensions. This visualization highlights regions influencing the model’s predictions, aligning with explainable artificial intelligence (XAI) principles. Experimental results demonstrate high detection accuracy and effective interpretability in indoor environments, making the framework suitable for robotics applications requiring both precision and transparency. The proposed method offers a practical and explainable solution for real-time scene understanding in intelligent systems.
Co-Authors Ahmed, Nasim al Huda, Md Sadi Al Mamun, Abdullah Al-Mamun, Abdullah Ali, Md. Asraf Al–Jubair, Md. Abdullah Badhon, Nasir Uddin Bani, Most. Momtahina Basiron, Halizah bin Zuber, Muhammad Amer Ridzuan Em, Poh Ping Ferdous, A.H.M. Iftekharul Hishamuddin, Muhammad Hoque, Jesmeen Mohd Zebaral Hossain, Ajran Hossen, Busrat Husni Zai, Mohamed Ashraf Ibrahim, Azmi Islam, Abtahi Islam, Aminul Islam, Md. Reazul Islam, Md. Safiul Jahan, Busrat Khan, Nur-e Sarjina Liew, Tze Hui Man, Abdullah Mand, Ali Afzalian Mridha, M. F. Musha, Ahmmad Noor, Khalid Sifulla Patwary, Md. Owafeeuzzaman Raja, Joseph Emerson Ramanath, Thirumalaimuthu Thirumalaiappan Ramanathan, Thirumalaimuthu Thirumalaiappan Ranjbari, Sima Sadib, Ridwan Jamal Sayeed, Md. Shohel Tahabilder, Anik Tawsif Khan, Chy. Mohammed Tuz Johora, Fatama Yusof, Ashaari