To address the challenge of accurately and efficiently detecting cells and injection needle tips with large scale discrepancies in automated microinjection, this paper proposes a high-efficiency instance segmentation model named DELD-YOLO. Built upon the YOLO11 architecture, the proposed model incorporates structurally re-parameterized diverse branch blocks into the backbone to enhance feature representation. An efficient lightweight multi-scale feature pyramid network is introduced to improve cross-scale feature fusion, and a detail-enhanced lightweight segmentation head is designed to preserve fine structural information while reducing computational cost. Experimental results demonstrate that DELD-YOLO outperforms the baseline YOLO11 in both cell and injector segmentation tasks, achieving mAP values of 98.7% for cells and 75.2% for injectors at IoU thresholds from 0.5 to 0.95. With only 2.08M parameters and 10.0 GFLOPs, the proposed model exhibits a favorable balance between accuracy and efficiency, making it well suited for automated cell injection and edge-device deployment.