南京航空航天大学学报（英文版）2024，Vol.41Issue(6)：710-724,15.DOI:10.16356/j.1005-1120.2024.06.004
基于多维注意和上采样融合的YOLO-v8雷达图像空中小目标检测
YOLO-v8 with Multidimensional Attention and Upsampling Fusion for Small Air Target Detection in Radar Images
摘要
Abstract
This study presents an innovative approach to improving the performance of YOLO-v8 model for small object detection in radar images.Initially,a local histogram equalization technique was applied to the original images,resulting in a notable enhancement in both contrast and detail representation.Subsequently,the YOLO-v8 backbone network was augmented by incorporating convolutional kernels based on a multidimensional attention mechanism and a parallel processing strategy,which facilitated more effective feature information fusion.At the model's head,an upsampling layer was added,along with the fusion of outputs from the shallow network,and a detection head specifically tailored for small object detection,thereby further improving accuracy.Additionally,the loss function was modified to incorporate focal-intersection over union(IoU)in conjunction with scaled-IoU,which enhanced the model's performance.A weighting strategy was also introduced,effectively improving detection accuracy for small targets.Experimental results demonstrate that the customized model outperforms traditional approaches across various evaluation metrics,including recall,precision,F1-score,and the receiver operating characteristic(ROC)curve,validating its efficacy and innovation in small object detection within radar imagery.The results indicate a substantial improvement in accuracy compared to conventional methods such as image segmentation and standard convolutional neural networks.关键词
YOLO/雷达图像/目标检测/机器学习Key words
YOLO/radar images/object detection/machine learning分类
信息技术与安全科学引用本文复制引用
江振宇,李晓东,杜晨,陈安,韩彦强,李金金..基于多维注意和上采样融合的YOLO-v8雷达图像空中小目标检测[J].南京航空航天大学学报（英文版）,2024,41(6):710-724,15.基金项目
This work was supported by the Na-tional Natural Science Foundation of China Joint Fund(No.U21B2028),the National Key R&D Program of China(No.2021YFC 2100100),and the Shanghai Science and Technology Project(Nos.21JC1403400,23JC1402300). （No.U21B2028）
