ABSTRACT Appearance quality inspection of fresh corn is a critical step to ensure product quality during its deep processing, but existing deep learning models often struggle to accurately detect subtle or multiscale defects. This study proposed a novel appearance quality detection method for fresh corn, in which a fresh corn detection model improved based on GELAN (FCD‐GELAN) was used to detect targets in images generated through weighted fusion of visible and near‐infrared images. By incorporating an efficient aggregation attention module into the baseline model, it enhanced the model's ability to extract and retain features in shallow network layers. Additionally, to optimize the model for focusing on subtle targets, pinwheel‐shaped convolution was employed to partially replace convolutional layers in the backbone and neck. Furthermore, a multiscale spatial attention module was added before the detection head to strengthen the model's perception of targets across different scales. Experimental data demonstrated that the proposed model achieves an m AP @0.5 of 90.2% on a self‐constructed dataset, with an inference time of 24.0 ms, meeting real‐time performance requirements. Compared to EfficientDet, D‐FINE, RT‐DETR, YOLOv10, and YOLOv11, the proposed model improved m AP @0.5 by 11.2%, 3.2%, 5.3%, 3.6%, and 4.0%, respectively, validating its superior performance in the fresh corn appearance quality detection. This paper provides an efficient detection approach for fresh corn appearance recognition that balances real‐time performance with accuracy.
Xu et al. (Sun,) studied this question.