基于特征对齐和关键点辅助激励的退化热成像图目标检测

在热成像图目标检测中,存在图像的纹理单一、目标边界模糊等退化现象,这造成目标定位困难、目标与预定义锚点框无法精准匹配等问题.因此,文中提出基于特征对齐和关键点辅助激励的退化热成像图目标检测算法.引入可见光图分支,计算2个分支指定层的特征差异,提升热成像域与可见光域之间的相似度.为了丰富网络高层中的目标细节信息,修改特征图级联和检测尺度.部署包含关键点辅助激励的无锚点检测器,较好地定位目标并学习预定义锚点框覆盖较差的实例.在2个数据集上的对比实验表明,文中算法可准确定位热成像目标,有效提升退化热成像图目标检测精度.

Object Detection in Degraded Thermal Image Based on Feature Alignment and Assisted Excitation of Key Points

In the object detection of thermal images,the image degradation phenomena,like the simple texture and the blurred object boundary,result in difficulties in localizing objects and matching the objects with the predefined anchor boxes.Therefore,an object detection algorithm for degraded thermal image based on feature alignment and assisted excitation of key points is proposed.Firstly,the visible image branch is introduced,and the similarity between the thermal domain and the visible domain is improved by calculating the feature difference of specified layers in two branches.Then,feature map concatenation and detection scale are modified to enrich the details of objects in the high-level network layers.Finally,an anchor-free detector with assisted excitation of key points is deployed,and thus the model localizes objects better and learn the instances poorly covered by the predefined anchor boxes.Comparative experiments on two datasets show that the proposed algorithm localizes thermal objects accurately and improves the accuracy of object detection in degraded thermal image effectively.