LTSCD-YOLO: A Lightweight Algorithm for Detecting Typical Satellite Components Based on Improved YOLOv8

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 16; p. 3101
• Main Authors: Tang, Zixuan, Zhang, Wei, Li, Junlin, Liu, Ran, Xu, Yansong, Chen, Siyu, Fang, Zhiyue, Zhao, Fuchenglong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Accuracy; Adaptability; Aerospace environments; Algorithms; Artificial satellites; Computer applications; Convolution; Datasets; Deep learning; Efficiency; Lightweight; lightweight network; Localization; Modules; Neural networks; Object recognition; Parameters; Remote sensing; Satellite imagery; Satellites; Simulation; Software; Space debris; typical satellite component detection; typical satellite components dataset; Weight reduction; YOLOv8
• ISSN: 2072-4292
• DOI: 10.3390/rs16163101

Typical satellite component detection is an application-valuable and challenging research field. Currently, there are many algorithms for detecting typical satellite components, but due to the limited storage space and computational resources in the space environment, these algorithms generally have the problem of excessive parameter count and computational load, which hinders their effective application in space environments. Furthermore, the scale of datasets used by these algorithms is not large enough to train the algorithm models well. To address the above issues, this paper first applies YOLOv8 to the detection of typical satellite components and proposes a Lightweight Typical Satellite Components Detection algorithm based on improved YOLOv8 (LTSCD-YOLO). Firstly, it adopts the lightweight network EfficientNet-B0 as the backbone network to reduce the model’s parameter count and computational load; secondly, it uses a Cross-Scale Feature-Fusion Module (CCFM) at the Neck to enhance the model’s adaptability to scale changes; then, it integrates Partial Convolution (PConv) into the C2f (Faster Implementation of CSP Bottleneck with two convolutions) module and Re-parameterized Convolution (RepConv) into the detection head to further achieve model lightweighting; finally, the Focal-Efficient Intersection over Union (Focal-EIoU) is used as the loss function to enhance the model’s detection accuracy and detection speed. Additionally, a larger-scale Typical Satellite Components Dataset (TSC-Dataset) is also constructed. Our experimental results show that LTSCD-YOLO can maintain high detection accuracy with minimal parameter count and computational load. Compared to YOLOv8s, LTSCD-YOLO improved the mean average precision (mAP50) by 1.50% on the TSC-Dataset, reaching 94.5%. Meanwhile, the model’s parameter count decreased by 78.46%, the computational load decreased by 65.97%, and the detection speed increased by 17.66%. This algorithm achieves a balance between accuracy and light weight, and its generalization ability has been validated on real images, making it effectively applicable to detection tasks of typical satellite components in space environments.