This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network(M-FCN)in strong sea clutter.Firstly,the constant false alarm rate(CFAR)detection method utilizes a low threshold ...This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network(M-FCN)in strong sea clutter.Firstly,the constant false alarm rate(CFAR)detection method utilizes a low threshold with high probability of false alarm to detect sea-surface weak targets after non-coherent integration.Reducing the detection threshold can generate a large number of false alarms while increasing the detection rate,and how to suppress a large number of false alarms is the key to improve the performance of weak target detection.Then,the detection result of the low threshold is operated to construct the target matrix suitable for the size of fully convolutional networks and the convolution operator form.Finally,the M-FCN architecture is designed to learn the different accumulation characteristics of the target and the sea clutter between different frames.For improving the detection performance,the historical multi-frame information is memorized by the network,and the end-to-end structure is established to detect sea-surface weak target automatically.Experimental results on measured data demonstrate that the M-FCN method outperforms the traditional track before detection(TBD)method and reduces false alarm tracks by 35.1%,which greatly improves the track quality.展开更多
传统的海面目标检测识别方法在复杂背景下存在目标检测率低、目标特征依赖人工设计等问题,很难满足实际应用的要求。本文以深度学习、智能芯片等技术为基础,针对可见光、遥感等多波段传感器成像,基于主流深度学习框架建立图像识别、目...传统的海面目标检测识别方法在复杂背景下存在目标检测率低、目标特征依赖人工设计等问题,很难满足实际应用的要求。本文以深度学习、智能芯片等技术为基础,针对可见光、遥感等多波段传感器成像,基于主流深度学习框架建立图像识别、目标检测网络模型,实现智能目标位置检测、目标分类及关键部位识别,采用通用智能芯片NPU(Neural Process Unit)搭建完成嵌入式环境下可见光场景舰船目标智能识别系统,实现智能目标识别算法在硬件资源受限环境下的高效处理,初步验证智能技术在飞行器上应用的可行性。展开更多
基金This was work supported by the National Natural Science Foundation of China(U19B2031).
文摘This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network(M-FCN)in strong sea clutter.Firstly,the constant false alarm rate(CFAR)detection method utilizes a low threshold with high probability of false alarm to detect sea-surface weak targets after non-coherent integration.Reducing the detection threshold can generate a large number of false alarms while increasing the detection rate,and how to suppress a large number of false alarms is the key to improve the performance of weak target detection.Then,the detection result of the low threshold is operated to construct the target matrix suitable for the size of fully convolutional networks and the convolution operator form.Finally,the M-FCN architecture is designed to learn the different accumulation characteristics of the target and the sea clutter between different frames.For improving the detection performance,the historical multi-frame information is memorized by the network,and the end-to-end structure is established to detect sea-surface weak target automatically.Experimental results on measured data demonstrate that the M-FCN method outperforms the traditional track before detection(TBD)method and reduces false alarm tracks by 35.1%,which greatly improves the track quality.
文摘传统的海面目标检测识别方法在复杂背景下存在目标检测率低、目标特征依赖人工设计等问题,很难满足实际应用的要求。本文以深度学习、智能芯片等技术为基础,针对可见光、遥感等多波段传感器成像,基于主流深度学习框架建立图像识别、目标检测网络模型,实现智能目标位置检测、目标分类及关键部位识别,采用通用智能芯片NPU(Neural Process Unit)搭建完成嵌入式环境下可见光场景舰船目标智能识别系统,实现智能目标识别算法在硬件资源受限环境下的高效处理,初步验证智能技术在飞行器上应用的可行性。
