AN IMPROVED RT-DETR FOR STABLE AND REAL-TIME DEFECTIVE EGG DETECTION IN EDGE COMPUTING ENVIRONMENTS

To achieve stable abnormal egg detection under edge device deployment conditions, this paper proposes a CSDE-DETR-R18 abnormal egg detection model based on an optimized RT-DETR-R18. The CSFM module is incorporated into the feature fusion path of the RT-DETR-R18 model to aggregate multi-scale features. Subsequently, the model's ability to extract low-contrast features is enhanced by replacing the standard convolutions in the backbone network with the DEConv module, which improves its recognition capability for abnormal egg characteristics such as light-colored spots. Finally, the NWD+AIOU mixed loss function is employed to improve the model's localization accuracy for minute targets (such as fine cracks, specks, etc.). The experimental results demonstrate that the CSDE-DETR-R18 model achieved mAP@0.5, P, and R of 87.8%, 88.9%, and 87.3%, respectively, representing improvements of 3.4, 3.1, and 0.5 percentage points over RT-DETR-R18. Test results from the Raspberry Pi deployment revealed that CSDE-DETR-R18 demonstrated a significant advantage in inference stability compared with the YOLOv8 series, one of the most advanced detectors currently available. This is of great engineering significance for tasks requiring stable timing performance at equal heights, such as robotic arm sorting.