摘要
由于红外探测器感应波段与可见光不同,不依赖于大气光的反射传播,而是取决于环境中物体自身散发的辐射强度,所以其在雾霾、夜晚等可见度低的条件下往往比可见光具有更好的目标检测效果。针对红外场景中目标检测精度低、实行性差的问题,提出一种基于注意力机制的红外目标检测方法。首先,设计一种轻量化网络结构;其次,采用注意力机制提高网络的特征提取能力;然后,改进迭代特征金字塔结构提高对不同尺度目标的检测能力;最后,在训练过程中引入complete intersection over union(CIoU)损失函数和梯度均衡机制(GHM)损失函数改善正负样本不平衡问题。与其他算法的对比实验结果表明,所提算法的检测精度和速度显著提高。
Because the sensing band of the infrared detector is different from the visible light,it does not depend on the reflection and propagation of atmospheric light,but depends on the radiation intensity emitted by the object itself in the environment,so it often has better target detection effect than the visible light under the conditions of low visibility such as haze and night.Aiming at the problems of low accuracy and poor practicality of target detection in infrared scene,an infrared target detection method based on attention mechanism is proposed.First,a lightweight network structure is designed;second,attention mechanism is used to improve the ability of network feature extraction;then,the iterative feature pyramid structure is improved to improve the detection ability of targets with different scales;finally,complete intersection over union(CIoU)loss function and gradient equilibrium mechanism(GHM)loss function are introduced in the training process to improve the imbalance of positive and negative samples.Compared with other algorithms,the experimental results show that the detection accuracy and speed of the proposed algorithm are significantly improved.
作者
顾星
詹伟达
崔紫薇
桂婷婷
石艳丽
胡家珲
Gu Xing;Zhan Weida;Cui Ziwei;Gui Tingting;Shi Yanli;Hu Jiahui(School of Electronic Information Engineering,Changchun University of Science and Technology,Changchun 130022,Jilin,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2023年第10期283-290,共8页
Laser & Optoelectronics Progress
基金
吉林省发展与改革委员会创新能力建设专项(FG2021236JK)。
关键词
成像系统
深度学习
卷积神经网络
目标检测
注意力机制
损失函数
imaging systems
deep learning
convolutional neural network
target detection
attention mechanism
loss function
作者简介
通信作者:詹伟达,zhanweida@cust.edu.cn。
