A Resolution and Localization Algorithm for Closely-Spaced Objects Based on Improved YOLOv5 Joint Fuzzy C-Means Clustering

Abstract

The presence of numerous space objects poses significant challenges to spacecraft launch, operation, and space security. To address this issue, a novel algorithm based on improved YOLOv5 joint Fuzzy C-Means (FCM) has been proposed for discerning and localizing closely-spaced objects (CSOs) in space. The algorithm employs the lightweight neural network YOLOv5 to estimate the quantity of the targets and employs the improved FCM localizing. In the first stage, it enhances the precision of CSOs quantity estimation by integrating small target layers and the Convolutional Block Attention Module (CBAM) into the YOLOv5 network. In the second stage, leveraging the estimated target quantity as input, the FCM algorithm based on Lanczos3 interpolation and particle distribution was used for the sub-pixel localization of each target cluster. The experimental results show that the algorithm's quantity estimation accuracy is more than 90%, and the average localization error is within 0.32 pixels. Moreover, the average running time of the algorithm is less than 0.034 s. Compared with other methods, the algorithm shows excellent performance, and its effectiveness is important for target tracking, 3D localization, and detailed analysis.

Keywords

• Closely-spaced objects
• YOLOv5
• interpolation
• particle distribution
• fuzzy C-mean algorithm
• resolution