机电工程技术2024，Vol.53Issue(5)：172-177,6.DOI:10.3969/j.issn.1009-9492.2024.00033
基于迁移学习和改进Faster-RCNN遥感影像飞机目标检测
Aircraft Target Detection Based on Transfer Learning and Improved Faster-RCNN Remote Sensing Image
摘要
Abstract
To improve the accuracy and generalization ability of aircraft target detection in remote sensing images,issues such as complex backgrounds,scale variations,dense targets,uncertain aircraft orientations,and subtle features need to be addressed.Due to the limited amount of training data at this stage,initial training consumes a significant amount of computational power and time,and is prone to overfitting.Therefore,it is necessary to optimize the model structure and training process.To address the aforementioned issues,a transfer learning strategy is introduced.Before training the Faster-RCNN model,pre-trained weights from the MS COCO dataset are loaded to enable rapid model convergence,saving a significant amount of training time.Then,the original Faster-RCNN's VGG16 feature extraction network is replaced with ResNet50 to better utilize deep-level semantic information.On this basis,the network's ability is enhanced to detect and localize targets by combining FPN networks,increasing the number of anchor boxes from 9 to 15 in the original Faster-RCNN,and by fusing multi-scale feature maps to obtain richer feature representations.Aircraft target detection experiments are conducted using the RSOD-Dataset as an example and the performance of different detection algorithms is compared.Additionally,the generalization and stability of the model using the NWPU VHR-10 dataset is validated.The experimental results demonstrate that the improved Faster-RCNN achieves a precision rate of 97.54%on the RSOD-Dataset,and 98.27%on the NWPU VHR-10 dataset.Through transfer learning and improving the network structure of Faster-RCNN,high-precision target detection with limited data and strong generalization ability can be achieved.The proposed method can be applied to other target detection and recognition tasks,demonstrating good generalization potential.关键词
遥感影像/迁移学习/目标检测/Faster-RCNN/深度学习Key words
remote sensing images/transfer learning/target detection/faster-RCNN/deep learning分类
信息技术与安全科学引用本文复制引用
周绍鸿,方新建,刘鑫怡,张潆丹,严盛..基于迁移学习和改进Faster-RCNN遥感影像飞机目标检测[J].机电工程技术,2024,53(5):172-177,6.基金项目
安徽省煤矿安全大数据分析与预警技术工程实验室开放基金(CSBD2022-2D04) （CSBD2022-2D04）
