数据集是基于深度学习语义分割技术的重要组成部分。为了将语义分割技术应用于野外战场环境,构建一个符合实战场景的数据集至关重要。针对迷彩伪装目标侦察识别的作战保障需求,分析了野外战场环境及战场侦察图像的特点,设计了特定场景数据集的构建流程与方法,构建了具有精细化语义标注的语义分割数据集CSS,并通过实验验证了该数据集在语义分割任务上的有效性。

Dataset is an important part of semantic segmentation technology based on deep learning. In order to apply semantic segmentation technology to the field battlefield environment, it is very important to construct a dataset that conforms to the actual combat scene. In this work, aiming at the operational support requirements for the detection and identification of camouflage targets, the characteristics of the field battlefield environment and battlefield reconnaissance images are analyzed, the construction process and method of the specific scene dataset are designed, and the semantic segmentation dataset CSS with refined semantic annotation is constructed. The effectiveness of the dataset on semantic segmentation tasks is verified by experiments.