Robust Object Detection in Industrial Safety Environments Using YOLOv8

In industrial and safety-critical environments, real-time object detection plays a crucial role in hazard prevention and operational efficiency. This shared task presents a high-performance object detection pipeline using YOLOv8 to identify three critical object categories: Fire Extinguishers, Toolboxes, and Oxygen Tanks. The system was trained and fine-tuned on a dataset of 1,139 annotated images with a balanced class distribution. The project focused on optimizing model performance through data augmentation, hyperparameter tuning, and occlusion handling. A final mAP@0.5 score of 85.1\% was achieved. The study addresses key challenges such as overfitting, occlusion-based misclassification, and lighting sensitivity. This work demonstrates an effective approach to deploying lightweight yet accurate object detection models in real-world industrial settings.