CAFDA-YOLO: A cross-axis fusion dual-attention object-detection model for blue straggler stars

Abstract Blue straggler stars (BSS), with anomalous luminosity and temperature relative to main-sequence turn-off stars, remain pivotal for advancing insights into the dynamics and evolution of stellar systems. However, traditional manual or semi-automatic methods for identifying BSS are still limited by subjective thresholds and inefficiency in large-scale sky survey projects. Moreover, publicly available BSS datasets specifically designed for object detection are conspicuously lacking. To address the above issues, we construct two labeled BSS datasets (completed by Chenyang Lv and Yuhao Shi) for object detection, derived from 3473 original images and segmented based on grayscale thresholds. Simultaneously, we propose CAFDA-YOLO, an object-detection model that combines convolutional neural networks and the Transformer architecture. This new framework introduces the cross-axis fusion integrator module, which fuses multi-directional features through group convolution, diagonal attention, and dual-attention mechanisms, thereby improving spatial context modeling. The experimental results show that CAFDA-YOLO consistently maintains the highest mAP in BSS detection across all datasets. In particular, it achieves an impressive best result in the low gray threshold dataset, with mAP@50 reaching 90.80% and mAP@75 being 10.69% higher than the baseline model. Multiple experiments indicate that CAFDA-YOLO has strong performance and robustness. This work provides a framework for the automatic detection of BSS and provides possibilities for future research on stellar interactions and stellar evolution.