全球定位系统2026，Vol.51Issue(2)：61-69,9.DOI:10.12265/j.gnss.2026118
AW-YOLO:一种结构引导与频域增强的陨石坑检测网络
AW-YOLO:a crater detection network with structure guidance and frequency domain enhancement
摘要
Abstract
High-precision and efficient automatic crater detection is crucial for planetary geological research,surface age estimation,and rover autonomous navigation.However,deep learning-based detection methods often suffer from insufficient feature representation when addressing challenges such as blurred crater edges and huge scale variations in planetary remote sensing images.To tackle these issues,this paper proposes a crater detection method for Martian thermal infrared remote sensing images.A Sobel edge-guided attention(SEGA)module is designed to alleviate the problems of blurred edges and degraded structures of craters.By introducing ring-shaped edge prior information based on the Sobel operator into the backbone network and combining it with a cross-attention mechanism,the network is guided to focus on the contour and structural features of craters.To address the large-scale span of craters and insufficient feature expression,a wavelet feature fusion module(WFM)is constructed.By integrating multi-resolution frequency-domain information extracted via wavelet transform with a channel attention mechanism,the module enhances the feature fusion capability for craters and effectively copes with extreme scale variations from mini-craters to large impact craters.Experimental results on the public Mars Day Crater Detection dataset demonstrate that,compared with the original YOLO series models and other mainstream detection models,the proposed AW-YOLO(you only look once with attention and wavelet)model achieves favorable performance while maintaining high inference efficiency.Specifically,its Precision,Recall and F1 score reach 0.88,0.87 and 0.87,respectively.关键词
陨石坑检测/特征融合/降采样优化/频域补充/遥感图像Key words
crater detection/feature fusion/downsampling optimization/frequency-domain complement/remote sensing images分类
信息技术与安全科学引用本文复制引用
王芹芹,穆京畅,顾天昊..AW-YOLO:一种结构引导与频域增强的陨石坑检测网络[J].全球定位系统,2026,51(2):61-69,9.基金项目
国家自然科学基金(62403264) （62403264）
中国博士后科学基金(2024M761556) （2024M761556）
青岛市自然科学基金(24-4-4-zrjj-94-jcb) （24-4-4-zrjj-94-jcb）
山东省博士后创新项目(SDCX-ZG-202400312) （SDCX-ZG-202400312）
青岛市博士后应用研究项目(QDBSH20240102029) （QDBSH20240102029）
青岛大学系统科学联合研究计划(XT2024202) （XT2024202）
