摘要：

在密集星场下的空间目标与背景特征高度相似, 检测中容易导致大量虚警的产生。此外, 空间目标在远距离探测条件下往往呈现暗弱特征, 且在运动过程中会受到高亮恒星遮挡, 造成检测困难、漏检率高。针对以上问题, 提出一种基于YOLOv8框架的大核深度不同层级可分离卷积, 结合灰度及连通域判别精细化目标分割的密集恒星背景暗弱空间目标检测模型FRR-YOLOv8。首先, 使用C2f卷积层替代原YOLOv8网络SPPF(Spatial Pyramid Pooling Fast)模块的普通卷积层, 通过将不同层级的特征图进行卷积, 使模型可以获得更多的上下文信息, 促进对小物体、低信噪比(SNR)目标的检测, 解决目标因暗弱特征导致的漏检率高的问题; 其次, 使用RTMdet(Real-Time Models for object detection)结构作为YOLOv8网络的头部网络(Head)部分, 通过在模型结构的基本单元引入大核深度可分离卷积增大感受野, 并平衡不同分辨率层级间计算量、参数量, 增加基本构建单元的特征提取能力, 并在该模块结合灰度及连通域判定将目标区域由整体框选精细定位至个体邻域, 解决因背景恒星密集带来的检测干扰。改进算法在仿真图像数据集和真实图像数据集上均进行了实验, 在SNR范围为0.5 dB~1 dB的图像数据集上, 均值平均精度(mAP@0.5)可达到94.6%, 相较于原始的YOLOv8模型提高了10.8百分点, 证明FRR-YOLOv8模型对暗弱空间目标检测的有效性。

关键词: YOLOv8, 深度学习, 空间目标, 密集背景, 低信噪比

Abstract:

The high similarity between spatial targets and background features in dense star fields can easily lead to numerous false alarms during detection. In addition, space targets often exhibit weak and dim features under long-distance detection conditions and are obstructed by bright stars during motion, resulting in difficult detection and high missed detection rates. In response to these issues, this study proposes a dense stellar background dark and weak spatial object detection model FRR-YOLOv8 based on the YOLOv8 framework, which uses a large kernel depth and separable convolutions at different levels and combines grayscale and connected domain discrimination to refine object segmentation. First, the C2f convolutional layer is used to replace the ordinary convolutional layer of the Spatial Pyramid Pooling Fast (SPPF) module in the original YOLOv8 network. By convolving feature maps at different levels, the model can obtain more contextual information, promote the detection of small objects and low signal-to-noise ratio targets, and solve the problem of high missed detection rate caused by weak target features. Second, the Real Time Models for object detection (RTMdet) structure is used as the head part of the YOLOv8 network. Large kernel depth separable convolutions are introduced into the basic units of the model structure to increase the receptive field, balance the computational and parameter requirements between different resolution levels, and increase the feature extraction ability of the basic construction units. In this module, the grey level and connected domain judgment are combined to finely locate the target area from the overall frame selection to the individual neighborhood, solving the detection interference caused by dense background stars. The improved algorithm is tested on both simulated and real image datasets. The mAP@0.5 index reaches 94.6% on image datasets with signal-to-noise ratios ranging from 0.5 dB to 1 dB, which is 10.8% higher than that of the original YOLOv8 model. This result proves that the FRR-YOLOv8 model can detect targets in dark and weak spaces.

Key words: YOLOv8, deep learning, space target, dense background, low Signal-to-Noise Ratio (SNR)