基于改进YOLOv11的水果成熟度检测OA

Aiming at the existing problems of insufficient accuracy,the difficulty of identification under complex backgrounds,and the obvious limitations of traditional methods in feature extraction in fruit ripeness detection,a fruit ripeness detection algorithm(AGLU-YOLOv11)based on improved YOLOv11 is proposed,to meet the demands for efficient data and reliable collection in fruit ripeness detection.AGLU-YOLOv11 designs the C3k2_AddBlock_CGLU module by optimizing the C3k2 module in the YOLOv11 backbone network and integrating CATM(Conv Additive Self-Attention)and CGLU(Convolutional Gated Linear Unit),and significantly enhances feature extraction capability and adaptability of multi-variety and multi-stage ripeness fruits.At the same time,the AFCA Attention Mechanism is introduced in the feature fusion stage to strengthen global feature expression and adaptability to complex backgrounds,and achieve efficient fruit quality detection and labeling.Experimental results show that AGLU-YOLOv11 performs better in precision,robustness and multi-scale object adaptability than other detection models in Precision,Recall,mAP@0.5 and mAP@0.5:0.95 indicators,and can better meet the demands for identifying fruit ripeness.