Zhao Beigeng, Song Rui, Zhou Ye, Yu Lizhi, Zhang Xia, Liu Jiren
School of Computer Science and Engineering, Northeastern University, Shenyang, China.
College of Public Security Information Technology and Intelligence, Criminal Investigation Police University of China, Shenyang, China.
Heliyon. 2024 May 10;10(10):e30485. doi: 10.1016/j.heliyon.2024.e30485. eCollection 2024 May 30.
The specificity of scenarios and tasks in Unmanned Aerial Vehicles (UAV)-based maritime rescue poses challenges for detecting targets within images captured by drones in such environments. This study focuses on leveraging heuristic methods to extract data features from specific UAV maritime rescue images to optimize the generation of anchor boxes in detection models. Experiments conducted on the large-scale SeaDronesSee maritime rescue dataset, using the MMDetection object detection framework, demonstrated that the optimized anchor boxes, improved model performance by 48.9% to 62.8% compared to the framework's default configuration, with the most proficient model surpassing the official highest SeaDronesSee baseline by over 49.3%. Further analysis of the results revealed the variation in detection difficulty for different objects within the dataset and identified the reasons behind these differences. The methodology and analysis presented in this study hold promise for optimizing UAV-based maritime rescue object detection models as well as refining data analysis and enhancement.
基于无人机的海上救援中场景和任务的特殊性，给在此类环境下检测无人机拍摄图像中的目标带来了挑战。本研究着重利用启发式方法从特定的无人机海上救援图像中提取数据特征，以优化检测模型中锚框的生成。使用MMDetection目标检测框架，在大规模的SeaDronesSee海上救援数据集上进行的实验表明，与框架的默认配置相比，优化后的锚框将模型性能提高了48.9%至62.8%，最出色的模型比官方最高的SeaDronesSee基线高出49.3%以上。对结果的进一步分析揭示了数据集中不同物体检测难度的差异，并确定了这些差异背后的原因。本研究中提出的方法和分析对于优化基于无人机的海上救援目标检测模型以及完善数据分析和增强方面具有前景。
