Edge-intelligent lightweight vision system for perspective-corrected egg-cage matching in cage-reared ducks

In the stereoscopic cage-rearing system, monitoring the individual egg production of laying ducks is essential for identifying low-yield individuals and optimizing breeding management. To overcome the high deployment cost of multi-sensor monitoring and the limited coverage of fixed cameras, this study proposes a mobile camera-based video monitoring method capable of automatically performing duck egg detection, cage QR code recognition, and egg-cage matching counting. Based on an improved YOLOv11 framework, a Lightweight Duck Egg and QR Code Detection model (LDEQ-OD) was developed. By integrating a Dual Detection Head (DH), a C3-DDF module, and a SENet attention mechanism, the model achieved notable improvements in small-object detection accuracy and real-time inference performance on edge devices. On the Jetson Nano platform, the model contained 1.4 M parameters and achieved an average inference time of 59.2 ms, with precision, recall, and mAP@0.5:0.95 reaching 99.6%, 99.3%, and 95.3%, respectively. The OC-SORT algorithm was employed for multi-object tracking to establish spatiotemporal associations between eggs and QR codes, while a Cascade Robust QR Code Decoding (CRQD) algorithm enhanced decoding accuracy under motion blur and uneven illumination. Compared with the traditional ZBar decoder, CRQD improved the overall Code Identification Rate from 72.7% to 99.3%, demonstrating significant robustness. Furthermore, a dynamic matching strategy based on the Minimum Aspect Ratio Deviation (MARD) was proposed to compensate for geometric distortion caused by camera tilt, achieving a Mean Absolute Error (MAE) of 0.017 eggs per cage and an Egg-Cage Matching Accuracy (ECMA) of 98.3%. Experiments under different motion speeds (0.14, 0.21, and 0.44 m/s) confirmed that the proposed matching algorithm maintained stable and reliable performance under complex geometric perspectives and varying camera movements. The system was deployed on the Jetson Nano platform at 10 frames per second and integrated with real-time data acquisition and visualization modules, enabling intelligent and real-time monitoring of individual egg production in cage-reared ducks to support precision breeding management.