计算机与现代化 ›› 2024, Vol. 0 ›› Issue (12): 78-83.doi: 10.3969/j.issn.1006-2475.2024.12.012

• 图像处理 • 上一篇    下一篇

基于改进YOLOv8的SAR舰船目标检测算法


  

  1. (上海航天电子技术研究所人工智能实验室,上海 201109)
  • 出版日期:2024-12-31 发布日期:2024-12-31
  • 基金资助:
    国防科技173计划技术领域基金资助项目(2021-JCJQ-JJ-0839)

SAR Ship Detection Algorithm Based on Improved YOLOv8

  1. (Artificial Intelligence Laboratory, Shanghai Aerospace Electronics Technology Research Institute, Shanghai 201109, China)
  • Online:2024-12-31 Published:2024-12-31

摘要: 为了提高SAR图像中舰船目标检测的准确性,特别是在面对目标大小不均、分布密集和复杂背景的挑战时,提出一种基于YOLOv8改进的YOLO-3M舰船目标检测算法。首先,算法引入多尺度膨胀卷积特征提取模块(Multiscale Dilated Convolution Block, MSDB)到主干网络中,使用多个膨胀率不同的卷积来提取多尺度特征,在不增加计算成本的情况下增大了感受野;其次,在颈部网络中引入多维度协作注意力机制(Multidimensional Collaborative Attention, MCA),在通道、高度和宽度3个维度上捕捉关键特征,实现不同维度信息的交互,帮助网络有效地关注到复杂背景中的关键部分;最后,在检测头引入MPDIoU损失函数,以应对现有损失函数在处理预测边界框与实际边界框时,尽管长宽比相同但宽度和高度数值完全不同时无法有效进行检测的问题。在SSDD数据集上的实验结果表明,本文算法在准确率和平均精度更高的同时,有效减少了参数量和计算量,使得模型更轻量并更适合于资源受限的环境,并且在复杂舰船的误检和漏检情况上有了显著的改善。

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, MPDIoU

Abstract: To enhance the accuracy of ship target detection in SAR images, especially when facing challenges such as uneven target sizes, dense distributions, and complex backgrounds, an improved YOLO-3M ship target detection algorithm based on YOLOv8 is proposed. Firstly, the algorithm introduces a Multi-Scale Dilated Convolution Block (MSDB) into the backbone network, 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 effectively 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.

Key words:  , ship detection; SAR image; YOLOv8; multi-scale dilated convolution block; multidimensional collaborative attention; MPDIoU

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