摘要
Small infrared target detection has widespread applications in various fields including military,aviation,and medicine.However,detecting small infrared targets in complex backgrounds remains challenging.To detect small infrared targets,we propose a variable-structure U-shaped network referred as CAFUNet.A central differential convolution-based encoder,ASPP,an Attention Fusion module,and a decoder module are the critical components of the CAFUNet.The encoder module based on central difference convolution effectively extracts shallow detail information from infrared images,complemented by rich contextual information obtained from the deep features in the decoder module.However,the direct fusion of the shallow detail features with semantic features may lead to feature mismatch.To address this,we incorporate an Attention Fusion(AF)module to enhance the network performance further.We performed ablation studies on each module to evaluate its effectiveness.The results show that our proposed algorithm outperforms the state-of-the-art methods on publicly available datasets.
作者
孙海蓉
康莉
HUANG Jianjun
SUN Hairong;KANG Li;HUANG Jianjun(Guangdong Key Laboratory of Intelligent Information Processing,Shenzhen,China;College of Electronics and Information Engineering,Shenzhen University,Shenzhen,Guangdong,China)
出处
《中国体视学与图像分析》
2023年第4期332-348,共17页
Chinese Journal of Stereology and Image Analysis
基金
国家自然科学基金(No.81960312,62171287)
深圳市科技项目(No.JCYJ20220818100004008)。