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作 者:王慧赢 王春平 付强 韩子硕 张冬冬 Wang Huiying;Wang Chunping;Fu Qiang;Han Zishuo;Zhang Dongdong(Department of Electronic and Optical Engineering,Shijiazhuang Campus of Army Engineering University,Shijiazhuang 050003,Hebei,China;232356 Troops of the Chinese People's Liberation Army,Xining 710003,Qinghai,China)
机构地区:[1]陆军工程大学石家庄校区电子与光学工程系,河北石家庄050003 [2]中国人民解放军32356部队,青海西宁710003
出 处:《光学学报》2023年第12期113-126,共14页Acta Optica Sinica
基 金:军内科研项目(KYSZJWJK2236)。
摘 要:针对现有基于深度学习的轻量级目标检测算法对复杂遥感场景图像中舰船目标检测精度低、检测速度慢的问题,提出了一种面向嵌入式平台的轻量级光学遥感图像舰船实时检测算法(STYOLO)。首先,针对主干网络内存访问成本较高的问题,利用高效网络架构ShuffleNet v2作为主干网络对图像进行特征提取,降低内存访问成本,提高网络并行度;其次,利用Slim-neck特征融合结构作为特征增强网络,以融合较低层级特征图中的细节信息,增强对小目标的特征响应,在多尺度信息融合区域施加坐标注意力机制,强化目标关注以提高较难样本检测以及抗背景干扰能力;最后,提出一种跨域迁移和域内迁移相结合的学习策略,减少源域与目标域的差异性,提升迁移学习效果。实验结果表明:基于光学遥感图像舰船检测公开数据集HRSC2016,与同类型快速检测算法YOLOv5s相比,所提算法的检测精度提高了2.7个百分点,参数量减少了61.77%,在嵌入式平台Jetson Nano上检测速度达到102.8 frame/s,能够有效实现对光学遥感图像中舰船目标的实时、准确检测。Objective Ship detection plays an important role in military and civilian fields such as defense security,dynamic port monitoring,and maritime traffic management.With the rapid development of space remote sensing technologies,the number of highresolution optical remote sensing images is increasing exponentially,which lays the data foundation for research on ship detection techniques.Meanwhile,it is required that detection systems should have realtime accuracy to match the growth rate of the number of remote sensing images.Traditional methods for object detection are mainly accomplished by the construction of mathematical models or the use of object saliency.However,most of these algorithms rely on the prior knowledge of experts and have certain limitations,which cannot cope with the complex and variable background and the multimodal and heterodyne objects.Recent years have seen the rapid development of deep learning technology.The object detection method based on convolutional neural networks(CNNs)is widely used because of its strong learning ability and high detection accuracy.Currently,mainstream object detection models based on deep learning are mainly divided into two categories,i.e.,twostage networks and singlestage networks.In general,twostage network detection has high accuracy but is difficult to deploy on embedded devices due to a large amount of computation and huge time consumption.The YOLO series,singlestage network detection algorithms,have received extensive attention and applications due to their simple network structure and consideration of both detection accuracy and detection speed.However,due to the poor computing power and limited memory resources of embedded devices,it is difficult to directly apply singlestage detection models to embedded devices to detect objects in real time.Hence,we expect to deploy a highperformance model to detect ships in optical remote sensing images on equipment terminals with limited resources and space and achieve a lightweight ship detection network for complex re
关 键 词:光学遥感图像 舰船检测 实时检测 嵌入式平台 注意力机制 迁移学习
分 类 号:TP751[自动化与计算机技术—检测技术与自动化装置]
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