Enhanced YOLO11 for lightweight and accurate drone-based maritime search and rescue object detection

Abstract

Accurately and rapidly detecting objects and their locations in drone-captured images from maritime search and rescue scenarios provides valuable information for rescue operations. The YOLO series, known for its balance between lightweight architecture and high accuracy, has become a popular method among researchers in this field. Recent advancements in the newly released YOLO11 model have demonstrated significant progress in general object detection tasks across everyday scenarios. However, its application to the specific task of drone-based maritime search and rescue still leaves substantial room for improvement. To address this gap, we propose targeted optimizations to enhance YOLO11’s performance in this domain. These include integrating a Space-to-Depth module into the Backbone, incorporating a content-aware upsampling algorithm in the Neck, and adding an extra detection head to better exploit shallow image features. These modifications significantly improve the model’s ability to detect small, overlapping, and rarely occurring objects, which are common challenges in maritime search and rescue tasks. Experimental evaluations conducted on the large-scale SeaDronesSee dataset demonstrate that the proposed optimized YOLO11 outperforms YOLOv8, YOLO11, and MambaYOLO across all scales. Moreover, under lightweight configurations, the model achieves substantial performance gains over YoloOW, a method renowned for its accuracy but depends on heavyweight configurations. In the lightweight complexity range, the proposed model achieves a relative accuracy improvement of 20.85% to 43.70% compared to these state-of-the-art methods. The code supporting this research is available at https://github.com/bgno1/sds_yolo11.