A parallel pipeline connected-component labeling method for on-orbit space target monitoring

Abstract

The paper designs a peripheral maximum gray difference (PMGD) image segmentation method, a connected-component labeling (CCL) algorithm based on dynamic run length (DRL), and a real-time implementation streaming processor for DRL-CCL. And it verifies the function and performance in space target monitoring scene by the carrying experiment of Tianzhou-3 cargo spacecraft (TZ-3). The PMGD image segmentation method can segment the image into highly discrete and simple point targets quickly, which reduces the generation of equivalences greatly and improves the real-time performance for DRL-CCL. Through parallel pipeline design, the storage of the streaming processor is optimized by 55% with no need for external memory, the logic is optimized by 60%, and the energy efficiency ratio is 12 times than that of the graphics processing unit, 62 times than that of the digital signal proccessing, and 147 times than that of personal computers. Analyzing the results of 8 756 images completed on-orbit, the speed is up to 5.88 FPS and the target detection rate is 100%. Our algorithm and implementation method meet the requirements of lightweight, high real-time, strong robustness, full-time, and stable operation in space irradiation environment.

A Lightweight YOLOv5-Based Model with Feature Fusion and Dilation Convolution for Image Segmentation

“…This proposed method combines feature fusion, dilation convolution, and MobileViT. Among them, feature fusion is a method by which to fuse feature maps of different scales, which can improve the detection ability of the model for small and distant targets [19,20]; dilation convolution is a method by which to capture a more extensive range of information by increasing the field of perception size in the spatial domain, which can effectively solve the problem of tiny structures existing inside the segmented objects [21,22]; MobileViT is a lightweight model that can achieve high-performance image segmentation through model compression and acceleration [23][24][25].…”