基于语义分割的红外图像天地线检测算法研究

Horizon Detection Based on Semantic Segmentation of Infrared Images

天地线检测在复杂场景的红外目标检测系统中具有重要的作用,将深度语义分割模型引入了天地线检测任务。不同于传统的基于手工模板的阈值分割算法,深度学习算法能够充分挖掘图像事例之间的结构信息与语义信息,具有较强的自适应性。文中利用Deeplab-v3+算法作为语义分割算法,实现了对天空与地面的有效分割。在城市、森林、山地等不同场景的实验中,所提算法取得了良好的天地线检测效果,验证了所提算法的有效性与鲁棒性。

The detection of horizon plays a significant role on the system of infrared dim small target detection on images with complex scenes.The deep learning semantic segmentation method is proposed to utilize in horizon detection task.Different from the traditional threshold segmentation method based on manual template,deep learning method digs the spatial information and semantic information of infrared images and has better adaptability.A new method is proposed to utilize the Deeplab-v3+as the semantic segmentation arithmetic to distinguish sky and ground.The experimental results of scenes with city,forest and mountain show that the proposed method can effectively detect the horizon and demonstrate the superiority robustness of the proposed approach.