MD Tamim Hossain

Ha Meem Hossain, Pritam Nath, Mahitun Nesa Mahi et al.

Vehicle detection systems trained on Non-Bangladeshi datasets struggle to accurately identify local vehicle types in Bangladesh's unique road environments, creating critical gaps in autonomous driving technology for developing regions. This study evaluates six YOLO model variants on a custom dataset featuring 29 distinct vehicle classes, including region-specific vehicles such as ``Desi Nosimon'', ``Leguna'', ``Battery Rickshaw'', and ``CNG''. The dataset comprises high-resolution images (1920x1080) captured across various Bangladeshi roads using mobile phone cameras and manually annotated using LabelImg with YOLO format bounding boxes. Performance evaluation revealed YOLOv11x as the top performer, achieving 63.7\% mAP@0.5, 43.8\% mAP@0.5:0.95, 61.4\% recall, and 61.6\% F1-score, though requiring 45.8 milliseconds per image for inference. Medium variants (YOLOv8m, YOLOv11m) struck an optimal balance, delivering robust detection performance with mAP@0.5 values of 62.5\% and 61.8\% respectively, while maintaining moderate inference times around 14-15 milliseconds. The study identified significant detection challenges for rare vehicle classes, with Construction Vehicles and Desi Nosimons showing near-zero accuracy due to dataset imbalances and insufficient training samples. Confusion matrices revealed frequent misclassifications between visually similar vehicles, particularly Mini Trucks versus Mini Covered Vans. This research provides a foundation for developing robust object detection systems specifically adapted to Bangladesh traffic conditions, addressing critical needs in autonomous vehicle technology advancement for developing regions where conventional generic-trained models fail to perform adequately.

Aparup Dhar, MD Tamim Hossain, Pritom Barua

As obesity rates continue to increase, automated calorie tracking has become a vital tool for people seeking to maintain a healthy lifestyle or adhere to a diet plan. Although numerous research efforts have addressed this issue, existing approaches often face key limitations, such as providing only constant caloric output, struggling with multiple food recognition challenges, challenges in image scaling and normalization, and a predominant focus on Western cuisines. In this paper, we propose a tailored solution that specifically targets Bangladeshi street food. We first construct a diverse dataset of popular street foods found across Bangladesh. Then, we develop a refined calorie estimation system by modifying the state-of-the-art vision model YOLOv8. Our modified model achieves superior classification and segmentation results, with only a slight increase in computational complexity compared to the base variant. Coupled with a machine learning regression model, our system achieves an impressive 6.94 mean absolute error (MAE), 11.03 root mean squared error (RMSE), and a 96.0% R^2 score in calorie estimation, making it both highly effective and accurate for real-world food calorie calculations.