类HED网络的热红外图像显著性人体检测深度网络

Similar HED-Net for Salient Human Detection in Thermal Infrared Images

热红外图像中的人体目标易于观察显著性强,应用广泛,但受限于热红外设备的硬件,往往图像中的人体目标边缘模糊,检测效果较差,同时因为热红外的特殊成像原理,人体目标检测时极易受到发热物和遮挡物的干扰,检测的精度也无法得到保证。针对上述问题,本文提出了一种类HED(holistically nested edge detection)的热红外显著性人体检测网络。网络采用类HED网络形式,通过将不同比例的空洞卷积编解码模块进行残差相加形式,完成人体目标的检测任务。实验证明该网络可以有效地检测人体目标,准确地预测边缘结构,同时在发热物及遮挡物等环境下也具有较高的检测精度。

Human targets in thermal infrared images are easy to observe and have a wide range of applications.However,they are limited by the hardware of thermal infrared devices.The edges of human targets in the images are often blurred and the detection efficiency is poor.Simultaneously,because of the special imaging principle of thermal infrared,human target detection is vulnerable to the interference of heating and occlusion objects and the detection accuracy cannot be guaranteed.In response to the above issues,this study proposes a type of holistically nested edge detection(HED)-thermal infrared saliency human detection network.The network adopted the form of a similar HED network and detected human targets by adding the residuals of different proportions of the hole convolutional codec module.Experiments showed that the network can effectively detect human targets,accurately predict the edge structure,and also have high detection accuracy in an environments with heating objects and obstructions.