Enhancing Robustness of Human Detection Algorithms in Maritime SAR through Augmented Aerial Images to Simulate Weather Conditions

TL;DR

This study enhances human detection in maritime SAR by using augmented aerial images to simulate weather conditions, improving YOLO model accuracy and robustness in diverse real-world scenarios.

Contribution

The paper introduces a data augmentation approach with simulated weather conditions to improve YOLO-based human detection in maritime SAR operations.

Findings

Models trained on augmented data achieved higher recall scores.

Augmentation improved robustness to weather, lighting, and contrast variations.

Detection accuracy increased by approximately 3.4% on YOLOv5l.

Abstract

7,651 cases of Search and Rescue Missions (SAR) were reported by the United States Coast Guard in 2024, with over 1322 SAR helicopters deployed in the 6 first months alone. Through the utilizations of YOLO, we were able to run different weather conditions and lighting from our augmented dataset for training. YOLO then utilizes CNNs to apply a series of convolutions and pooling layers to the input image, where the convolution layers are able to extract the main features of the image. Through this, our YOLO model is able to learn to differentiate different objects which may considerably improve its accuracy, possibly enhancing the efficiency of SAR operations through enhanced detection accuracy. This paper aims to improve the model's accuracy of human detection in maritime SAR by evaluating a robust datasets containing various elevations and geological locations, as well as through data augmentation which simulates different weather and lighting. We observed that models trained on augmented datasets outperformed their non-augmented counterparts in which the human recall scores ranged from 0.891 to 0.911 with an improvement rate of 3.4\% on the YOLOv5l model. Results showed that these models demonstrate greater robustness to real-world conditions in varying of weather, brightness, tint, and contrast.