在轨高效目标检测加速技术

针对深度卷积网络目标检测算法参数量大、计算量大以及受星上计算资源、存储资源及功耗的限制，难以实现在轨部署的问题，提出了一种在轨高效目标检测算法加速框架与实现方法。首先，设计了一种可以同时兼容三种卷积算子的计算引擎，有效提高了资源利用率;其次，从通道和卷积核两个维度将目标检测算法模型展开，实现了加速器的高度并行化和可扩展性;最后，在多种FPGA平台上实现了该加速器并对其性能进行了评估。实验结果表明：所提出的加速器计算性能可以达到1843.2 GFLOPs(每秒千兆次浮点运算)，推理时间为0.22 ms。与同类加速器方案相比，所提出的加速器框架在性能、功耗、能效比及推理时间方面具有很大优势，适合部署在资源受限环境中，具有良好的星上应用前景和价值。     

月表陨坑检测轻量化深度学习方法

针对目前基于深度学习的陨坑检测方法存在的模型参数量大和检测速度慢的问题，提出了一种轻量化的深度学习陨坑检测方法。首先，采用通道剪枝方法删减卷积神经网络中冗余的卷积核，得到结构紧凑高效的陨坑检测模型。然后，使用轻量化的深度可分离卷积操作替换基础陨坑检测模型中的标准卷积操作，进一步降低了模型的复杂度。仿真实验结果表明，所提出的轻量化陨坑检测模型能够保证较高的像素预测精度，并且能够适应亮度、图像噪声等干扰因素的影响。同时，与轻量化处理前的模型相比，参数量减少了99.2%，检测速度提升了94%。     

基于改进的卷积神经网络的步态识别

为了提高惯性传感器采集到的序列数据中步态识别的准确率，建立了一个激励层改进的卷积神经网络(CNN)模型。针对三轴加速度传感器对运动太过敏感导致步态周期划分不准确的问题，采用加速度传感器与弯曲度传感器组合获取人体运动信息。将CNN模型中激励层的线性整流函数（ReLU）改进为带泄露线性整流函数(Leaky ReLU)，以解决遇到卷积输出数据小于0时神经元被抑制的问题，进而达到提高步态识别准确率的目的。实验结果表明：激励层优化的CNN模型在行走、上下楼和上下坡五种步态模式下识别率达到了95.79%，与未采用弯曲度传感器的改进CNN模型和未进行激励层改进的CNN模型相比，步态识别率有所提高。     

A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification

Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit(APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So,actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network(HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term M...     

OGSRN: Optical-guided super-resolution network for SAR image

Although Convolutional Neural Networks(CNNs) have significantly improved the development of image Super-Resolution(SR) technology in recent years, the existing SR methods for SAR image with large scale factors have rarely been studied due to technical difficulty. A more efficient method is to obtain comprehensive information to guide the SAR image reconstruction.Indeed, the co-registered High-Resolution(HR) optical image has been successfully applied to enhance the quality of SAR image due to it...