The maintenance of the road infrastructure is vital to ensure public safety and the efficiency of transportation, but the unpredictable and widespread occurrence of potholes often hinders continuous monitoring. Traditional detection approaches typically rely on expensive equipment or manual inspections, rendering them impractical for large-scale, real-time deployment. To overcome these limitations, we present a low-cost and scalable pothole assessment framework that leverages the built-in LiDAR sensors available in modern smartphones in combination with a hybrid deep learning pipeline. The proposed system fuses RGB imagery with LiDAR-guided refinement to achieve precise depth estimation and accurate pothole segmentation, further enhanced through post-processing for improved boundary alignment. A comprehensive dataset of 25,000 RGB–LiDAR image pairs, collected under various road and lighting conditions, was curated to train and evaluate the framework. Extensive experiments demonstrate that our approach achieves high accuracy in both depth estimation and pothole delineation, significantly outperforming existing methods. This work highlights the potential of lightweight smartphone-based solutions for real-time automated road condition assessment, offering a practical and economical tool for modern infrastructure management.

Background: Chronic diseases such as diabetes and heart disease are rising in South Asia, with poor diet a major contributor. Most dietary apps are designed for Western foods and struggle with culturally diverse dishes such as Pakistani cuisine. Objective: This study aimed to build a machine learning-based system that can recognise Pakistani dishes, estimate portion sizes, and calculate their nutritional value. Methods: We developed a multi-stage automated dietary assessment pipeline for Pakistani cuisine using a multi-source dataset covering 83 dish classes across 10 categories. Following data cleaning, class balancing, and targeted augmentation, deep learning architectures were evaluated for food classification and segmentation. ResNet-50, MobileNetV2, and YOLOv8 were used for food recognition, while YOLOv8-Seg and Mask R-CNN enabled pixel-level segmentation for portion and nutrient estimation. All models were optimized using transfer learning and evaluated using standard classification and mask-quality metrics, followed by mobile application usability testing. Results: A multi-source dataset of ten categories and 83 Pakistani dishes was used to train and compare deep learning models for food classification and segmentation. Portion size was estimated using a segmentation- and depth-based approach without a reference object, while nutritional values were derived from standardised recipes and established food databases. The models were integrated into a mobile application for usability testing. YOLO-based models achieved the best balance of accuracy and speed for real-time classification and segmentation. Portion estimates were reasonably close to measured values, and usability testing showed the app was easy to use, with minor improvements suggested. Conclusions: Overall, this work demonstrates the feasibility of an AI-based dietary assessment system adapted to Pakistani cuisine, with future work focused on reducing processing demands and improving depth-based portion estimation.

Background: Long-term health conditions such as diabetes and heart disease are rising quickly, especially in South Asia, and poor diet is a major contributor. Existing dietary tracking apps are mostly built for Western food and struggle with mixed, culturally diverse dishes such as those found in Pakistani cuisine. Objective: This study aimed to build a machine learning-based system that can recognise Pakistani dishes, estimate portion sizes, and calculate their nutritional value. Methods: We developed a multi-stage automated dietary assessment pipeline for Pakistani cuisine using a newly compiled, multi-source dataset spanning 85 dish classes across 10 categories. Following data cleaning, class balancing, and targeted augmentation, multiple deep learning architectures were evaluated for classification and segmentation tasks. Specifically, ResNet-50, MobileNetV2, and YOLOv8 were used for food recognition, while YOLOv8-Seg and Mask R-CNN provided pixel-level segmentation for downstream portion and nutrient estimation. All models were optimized using transfer learning and evaluated on standard classification and mask-quality metrics. At the end, usability testing of mobile application was conducted. Results: A dataset of Pakistani food images across ten categories and 85 dishes was collected from multiple sources and used to train and compare several deep learning models for food classification and segmentation. Portion size was estimated using a segmentation and depth-based approach that does not need any reference object in the photo, and nutritional values were calculated using standardised recipes matched against established food databases. The models were then combined into a mobile application and tested with users for real-world usability. Among the classification models, a YOLO-based architecture performed best, and a related YOLO model gave the most practical results for segmentation, offering a good balance between accuracy and speed suited to real-time use on a phone. Portion size estimates were reasonably close to actual measured values, and early usability testing showed that users found the app easy to use, although some improvements were suggested. Conclusion: Overall, this work shows that a culturally adapted, AI-based dietary assessment system for Pakistani food is achievable, and it lays the groundwork for further development, particularly around reducing processing demands and improving the reliability of depth-based portion estimation.