Advanced Object Detection for Maritime Fire Safety

Abstract

In this study, we propose an advanced object detection model for fire and smoke detection in maritime environments, leveraging the DETR (Detection with Transformers) framework. To address the specific challenges of shipboard fire and smoke detection, such as varying lighting conditions, occlusions, and the complex structure of ships, we enhance the baseline DETR model by integrating EfficientNet-B0 as the backbone. This modification aims to improve detection accuracy while maintaining computational efficiency. We utilize a custom dataset of fire and smoke images captured from diverse shipboard environments, incorporating a range of data augmentation techniques to increase model robustness. The proposed model is evaluated against the baseline DETR and YOLOv5 variants, showing significant improvements in Average Precision (AP), especially in detecting small and medium-sized objects. Our model achieves a superior AP score of 38.7 and outperforms alternative models across multiple IoU thresholds (AP50, AP75), particularly in scenarios requiring high precision for small and occluded objects. The experimental results highlight the model’s efficacy in early fire and smoke detection, demonstrating its potential for deployment in real-time maritime safety monitoring systems. These findings provide a foundation for future research aimed at enhancing object detection in challenging maritime environments.