摘要
目的针对合成孔径雷达(synthetic aperture radar,SAR)图像舰船检测中因背景复杂、目标尺寸各异等因素导致的漏检、误检结果,提出一种基于YOLOv8(you only look once v8)的改进算法。方法首先,轻量化处理YOLOv8的原有网络结构,大幅降低网络的冗余度,使轻量化的网络更适合SAR图像舰船检测任务。其次,在主干网络中融入可变形卷积,增强模型对目标的感知能力,能更好地适应目标形变和复杂背景;同时,在颈部网络融入卷积注意力模块,减弱背景信息的干扰,使网络更专注舰船目标的特征。最后,采用EIoU(efficient intersection over union)损失函数,最小化预测框与真实框间的差值(包括宽度和高度),实现更快的收敛速度。结果分别在SSDD(SAR ship detection dataset)和HRSID(high-resolution SAR images dataset)上进行测试,结果表明,改进算法的检测性能优于当前几种流行的目标检测算法。其中,与YOLOv8相比,在两个公开数据集上,改进算法的精度评估指标mAP(mean average precision)@0.5分别提升0.68%和1.29%,mAP@0.75分别提升3.32%和3.10%,其处理速度FPS(frames per second)分别提升22帧/s和18帧/s。结论本文在轻量化处理YOLOv8基础上融合可变形卷积与注意力机制构建的改进算法,能实现SAR舰船检测精度和速度的双重提升。
Objective Synthetic aperture radar(SAR)has recently been widely used in fields such as maritime monitoring,military intelligence acquisition,and maritime management,primarily due to its capability to acquire data at any time under all weather conditions.Algorithms with better performance not only help improve ocean monitoring and navigation safety but also play a key role in areas such as maritime rescue,border security,and ocean resource management.Ship target detection methods can be divided into two categories:those based on deep learning and traditional methods.Deep learning methods offer high accuracy and strong generalization capabilities.These methods can be further classified into two categories:one-stage detection and two-stage detection.Compared to two-stage detection methods,one-stage detection methods generally achieve faster detection speeds at the expense of lower detection accuracy.One-stage detection methods,such as YOLO and single shot multibox detector(SSD),extract features through a backbone network,followed by direct classification and spatial position regression.Two-stage detection methods,such as R-CNN(region-based convolutional neural network)and Fast R-CNN,typically involve initial region generation followed by final region classification and regression.Currently,an increasing number of scholars are focusing on deep learning-based algorithms for ship target detection using SAR images.However,most of these methods have struggled to achieve an optimal balance between detection accuracy and processing efficiency.In this study,a lightweight model based on YOLOv8 was proposed to improve the performance of SAR ship detection while considering the balance between detection accuracy and efficiency.Method This study proposed a new method that substantially improved YOLOv8,called LDCE(lightweight-deformable convolution-CBAM-EIoU)-YOLOv8.The network structure of YOLOv8 was initially reconstructed to reduce network redundancy while maintaining sensitivity to ship features in SAR images.Furthermore,the introduction of deformable convolutional(DConv)allows the network to better perceive the environmental information around ship targets,thereby improving its capability to understand and capture ship these targets.Convolutional block attention module(CBAM)was introduced to minimize the interference of background information,enabling the network to focus on the key features of ship targets.Additionally,the efficient intersection over union(EIoU)loss function was adopted to enhance the convergence speed of the model.The experiments were initially conducted using the publicly available SAR ship detection dataset(SSDD),which comprises 1160 images with an average size of 500×500 pixels and a total of 2587 instances of ship targets.SSDD was randomly divided into training and testing sets in a ratio of 8∶2.During the training process,the size of the input images was adjusted to 640×640 pixels.The batch size and initial learning rate were set to 32 and 0.001,respectively.Meanwhile,the momentum and weight decay coefficients were 0.937 and 0.0005,respectively.Multiple ablation experiments were conducted to validate the effectiveness of the newly proposed model,using the original YOLOv8 as baseline for comparison.Furthermore,additional comparisons were conducted with other recently proposed methods(i.e.,Yang’s method and MSSDNet)as well as widely used detection algorithms,including Faster R-CNN,SSD,RetinaNet,YOLOv5,and YOLOv6.Additional experiments were conducted on the high-resolution SAR images dataset for ship detection(HRSID)to further validate the effectiveness and generalization of LDCE-YOLOv8.This dataset contains 5604 images with an average size of 800×800 pixels and a total of 16951 instances of ship targets.Result The accuracy evaluation indexes mAP@0.5 and mAP@0.75 for YOLOv8(the baseline)were 98.16%and 82.46%,respectively,while the frames per second(FPS,as speed evaluation index)was 263 frame/s.For LDCE-YOLOv8,the accuracy evaluation indexes mAP@0.5 and mAP@0.75 were 98.84%and 85.78%,respectively,and the speed evaluation index FPS was 285 frame/s.The parameter count of LDCE-YOLOv8 decreased by 24.58% compared to YOLOv8.The mAP@0.5,mAP@0.75,and FPS of LDCEYOLOv8 were 0.62%,2.23%,and 18.30% higher than those of MSSDNet,while these indexes were 0.90%,2.96%,and 4.40% higher than those of Yang’s method,respectively.The iterative curve of the bounding box loss values for each training session in the ablation experiment showed that LDCE-YOLOv8 had the lowest loss value and the fastest iteration speed.Overall,the results clearly showed that the newly proposed model(i.e.,LDCE-YOLOv8)exhibited the best detection performance in terms of parameter,precision,recall,average precision,and FPS.These results indicate a higher feature extraction capability of LDCE-YOLOv8 for detecting ship targets in SAR images.Detection result graphs for five representative scenarios were presented to compare the detection performance among different methods intuitively.In each case,LDCE-YOLOv8 achieved the best performance,accurately detecting all ship targets across these different scenarios.Consequently,the newly proposed method demonstrated strong anti-interference capability when handling substantial irregular noise distributed in SAR images.Moreover,this method suppressed false alarms from strong bright spots that had high similarity to ship features,while also performing well in small object detection.Whether in complex nearshore scenes or simpler sea scenes,LDCE-YOLOv8 effectively reduced missed detections and false detections while maintaining a high level of detection confidence.Additionally,the newly proposed method achieved better experiment results on the HRSID dataset,with mAP@0.5 at 88.91%,mAP@0.75 at 73.74%,and FPS at 312 frame/s,representing improvements of 1.29%,3.13%,and 6.1%compared to YOLOv8.Accordingly,the results with HRSID demonstrated the superior detection performance of LDCE-YOLOv8 in SAR ship detection.Conclusion In this study,a lightweight model based on YOLOv8 was proposed to enhance SAR ship detection in terms of accuracy and efficiency.This model,named LDCEYOLOv8,incorporates deformable convolution and an attention mechanism.Specifically,LDCE-YOLOv8 features substantially reduced network redundancy while maintaining its capability to capture ship features in SAR images.The integration of deformable convolution enhances the network’s capability to perceive environmental information around ship targets.Additionally,convolutional block attention modules and an efficient intersection over union loss function were incorporated to enhance target location.Experiments on two publicly available datasets(i.e.,SSDD and HRSID)validated the effectiveness of LDCE-YOLOv8 in SAR ship detection.However,the newly proposed algorithm still exhibits limitations,particularly in accurately detecting all ship targets in SAR images with densely packed ships.Ongoing investigations are focused on addressing this specific challenge.
作者
余光浩
陈润霖
徐金燕
徐前祥
王大寒
陈峰
Yu Guanghao;Chen Runlin;Xu Jinyan;Xu Qianxiang;Wang Dahan;Chen Feng(College of Computer and Information Engineering,Xiamen University of Technology,Xiamen 361024,China;Fujian Key Laboratory of Pattern Recognition and Image Understanding,Xiamen 361024,China;Island Research Center,Ministry of Natural Resources,Pingtan 350400,China;Shenzhen Smart Cities Technology Development Group Co.,Ltd.,Shenzhen 518036,China;Big Data Institute of Digital Natural Disaster Monitoring in Fujian,Xiamen 361024,China)
出处
《中国图象图形学报》
北大核心
2025年第3期724-736,共13页
Journal of Image and Graphics
基金
厦门理工学院研究生科技创新计划项目(YKJCX2023063)
福建省自然科学基金项目(2021J011190)。
