Thermal Infrared Salient Human Detection Model Combined with Thermal Features in Airport Terminal  

融合热特征的机场航站楼热成像显著人体检测模型

作　　者：YU Yuecheng LIU Chang WANG Chuan SHI Jinlong 於跃成;刘畅;汪川;史金龙(江苏科技大学计算机学院,镇江212100)

机构地区：[1]School of Computer,Jiangsu University of Science and Technology,Zhenjiang 212100,P.R.China

出　　处：《Transactions of Nanjing University of Aeronautics and Astronautics》2022年第4期434-449,共16页南京航空航天大学学报（英文版）

基　　金：supported in part by the National Key Research and Development Program of China(No. 2018YFC0309104);the Construction System Science and Technology Project of Jiangsu Province (No.2021JH03)。

摘　　要：Target detection in low light background is one of the main tasks of night patrol robots for airport terminal.However,if some algorithms can run on a robot platform with limited computing resources,it is difficult for these algorithms to ensure the detection accuracy of human body in the airport terminal. A novel thermal infrared salient human detection model combined with thermal features called TFSHD is proposed. The TFSHD model is still based on U-Net,but the decoder module structure and model lightweight have been redesigned. In order to improve the detection accuracy of the algorithm in complex scenes,a fusion module composed of thermal branch and saliency branch is added to the decoder of the TFSHD model. Furthermore,a predictive loss function that is more sensitive to high temperature regions of the image is designed. Additionally,for the sake of reducing the computing resource requirements of the algorithm,a model lightweight scheme that includes simplifying the encoder network structure and controlling the number of decoder channels is adopted. The experimental results on four data sets show that the proposed method can not only ensure high detection accuracy and robustness of the algorithm,but also meet the needs of real-time detection of patrol robots with detection speed above 40 f/s.弱光背景下的目标检测是航站楼夜间巡检机器人的主要任务之一。然而,那些能够在计算资源有限的机器人平台运行的算法往往难以确保航站楼中人体目标的检测精度。为此,本文提出了一种融合热特征的显著人体检测模型。该模型仍然以U-Net神经网络作为基本架构,但是在解码器模块结构和模型轻量化方面重新进行了设计。一方面,在模型的解码器部分增加了由热特征分支和显著特征分支构成的融合模块,进而设计对图像高温区域更为敏感的预测损失函数,以提升算法在复杂场景下的检测精度;另一方面,通过精简编码器网络结构和控制解码器通道数的方式对模型进行了轻量化改进,以降低算法对计算资源的需求。4个数据集上的实验结果表明,本文方法既能确保较高的检测精度和很好的算法鲁棒性,又能以40 f/s以上的检测速度满足巡检机器人实时检测的需要。

关 键 词：thermal infrared image human body detection SALIENCY thermal features lightweight model 

分 类 号：TN391[电子电信—物理电子学]