Enhancing life jacket detection for maritime search and rescue using YOLO models and illumination-robust preprocessing techniques

Abstract

Abstract Maritime Search and Rescue (SAR) operations are often challenged by vast search zones, poor visibility, and extreme lighting conditions, especially during nighttime missions. This study investigates the use of computer vision and object detection algorithms to automate life jacket detection and improve SAR effectiveness. To address the absence of domain-specific datasets, a custom image dataset featuring multiple life jacket types was developed. A two-fold methodology was adopted: evaluating the performance of YOLO object detection models (versions 5 through 12) on the dataset, and incorporating advanced image preprocessing techniques to enhance detection under challenging lighting conditions. The results demonstrate that preprocessing significantly improves detection performance in both overexposed and underexposed scenarios. Among all evaluated models, YOLOv10 achieved the strongest combination of precision and real-time inference speed (43.9 FPS on Tesla T4 GPU), making it a promising candidate for time-sensitive rescue applications. While individual cells of Tables 5, 6, 7, 8 and 9 show other detectors achieving higher precision under specific lighting × preprocessing combinations, YOLOv10 offers the best aggregate trade-off across the evaluated criteria. This work contributes a scalable benchmark solution for improving SAR outcomes by enabling faster and more reliable identification of individuals in distress at sea.