In Sabah and Sarawak, Malaysia, log transportation by rivers poses risks due to log fragmentation. This can obstruct water flow, causing navigation problems and flood risks during heavy rainfall. Current monitoring methods involve personnel at checkpoints but are slow. This project proposes an AI-based system using YOLO-v5 to detect intact logs and fragments on river surfaces. A dataset will be created by scraping websites and using Google Colab commands to download relevant keywords. Preprocessing includes data augmentation, contrast adjustment, noise reduction, and resolution standardization. The model is trained in Google Colab and integrated into a warning system using Thonny IDE. Performance metrics like precision, recall, F1 score, and confusion matrix are generated. By automating monitoring through AI, this project aims to improve safety and sustainability in Malaysian river log transportation.
