计算机与现代化Issue(12)：78-83,6.DOI:10.3969/j.issn.1006-2475.2024.12.012
基于改进YOLOv8的SAR舰船目标检测算法
SAR Ship Detection Algorithm Based on Improved YOLOv8
摘要
Abstract
To enhance the accuracy of ship target detection in SAR images,especially when facing challenges such as uneven tar-get sizes,dense distributions,and complex backgrounds,an improved YOLO-3M ship target detection algorithm based on YO-LOv8 is proposed.Firstly,the algorithm introduces a Multi-Scale Dilated Convolution Block(MSDB)into the backbone net-work,which uses convolutions with different dilation rates to extract multi-scale features,thereby enlarging the receptive field without increasing computational costs.Secondly,a Multidimensional Collaborative Attention(MCA)mechanism is incorporated into the neck network to capture key features across the channel,height,and width dimensions,facilitating interaction between different dimensional information and helping the network to effectively focus on key parts within complex backgrounds.Finally,an MPDIoU loss function is introduced in the detection head to address issues with existing loss functions that struggle to effec-tively detect when the predicted bounding box and the actual bounding box have the same aspect ratio but completely different widths and heights.Experimental results on the SSDD dataset show that the YOLO-3M algorithm achieves higher accuracy and average precision while effectively reducing the number of parameters and computational requirements,making the model more lightweight and suitable for resource-constrained environments.Additionally,there is a significant improvement in reducing false positives and false negatives in complex ship detection scenarios.关键词
舰船检测/SAR图像/YOLOv8/多尺度膨胀卷积模块/多维度协作注意力机制/MPDIoUKey words
ship detection/SAR image/YOLOv8/multi-scale dilated convolution block/multidimensional collaborative atten-tion/MPDIoU分类
信息技术与安全科学引用本文复制引用
谷岳,邓松峰,沈霁,穆文涛,赵恩棋..基于改进YOLOv8的SAR舰船目标检测算法[J].计算机与现代化,2024,(12):78-83,6.基金项目
国防科技173计划技术领域基金资助项目(2021-JCJQ-JJ-0839) （2021-JCJQ-JJ-0839）
