<p>High-energy charged particles in cosmic ray (CR) generate anomalous signals or noise artifacts when colliding with astronomical detectors, introducing distortions in both imaging and spectral data. This phenomenon poses a significant challenge in differentiating celestial signatures from cosmic ray-induced artifacts, particularly during observation scenarios involving resolved galaxies. In this study, we propose a deep learning framework that integrates the multi-scale attention mechanism and dynamic adaptive loss function for CR detection. The multi-scale linear attention mechanism is adopted to achieve a synergistic perception of global context modelling and local texture features in resolved galaxies. The large kernel selection block is used to effectively extend the capture range of global dependence of CR structures. The efficient multi-scale attention block is introduced to further enhance the texture differentiation between CR and celestial fringes of resolved galaxies. Furthermore, a dynamic weighted loss function based on a Gaussian residual response is introduced, with the aim of adjusting the negative sample gradient weights through statistical analysis of background noise patterns adaptively. This approach significantly improves model performance in class-imbalanced CR detection tasks. Experimental results demonstrate that the proposed model achieves consistent performance improvements on enhancements in CR detection for resolved galaxies.</p>