This study addresses the inefficiencies, subjectivity, and poor adaptability to lighting variations inherent in traditional candling methods used in large-scale egg incubation. We developed a high-throughput transmissive imaging system capable of capturing 30 eggs simultaneously. Based on this system, we propose PDA-YOLO, an enhanced YOLOv8-based object detection model featuring a position-decoupled attention strategy. Specifically, a lightweight C2f-SE module is integrated into the backbone to amplify subtle feature responses in low-contrast regions, while a CBAM is deployed prior to the detection head to mitigate background clutter through precise spatial attention. Experimental results on a self-constructed Hailan White egg dataset show that at the critical 60 h incubation stage, PDA-YOLO achieves a Recall of 91.5% and an mAP@0.5 of 97.4%, outperforming the YOLOv8 baseline while maintaining a real-time inference speed of 62.1 FPS. Grad-CAM visualizations confirm the model’s ability to focus on vascular textures and suppress noise. Furthermore, the model demonstrates robust performance under varying illumination (180–540 lumens), effectively mitigating missed detections in low light and recognition degradation from overexposure. This work provides a scalable, real-time solution for non-destructive, early-stage detection of poultry health and fertilization status in commercial hatcheries.