基于改進Y0L0v11的水果成熟度檢測
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中圖分類號：TP3191.4；TP183 文獻標識碼：A文章編號：2096-4706（2025）08-0034-07
Abstract：Aimingat theexisting problems of insufficient accuracy，the difficultyof identificationundercomplex backgrounds，ndthebvious limitationsof traditionalmethods infeatureextractioninfruitripenessdetection，afruitripenes detection algorithm （AGLU-YOLOv11） basedon improved YOLOv1 is proposed，to meet the demands for effcient data and reliable colection in fruit ripeness detection.AGLU-YOLOv11 designs the C3k2_AddBlock_CGLU module byoptimizing the C3k2 module in the YOLOv1l backbone network and integrating CATM（Conv Additive Self-Attention） and CGLU （Convolutional Gated Liear Unit），and significantlyenhances feature extraction capabilityand adaptabilityof multi-variety andmulti-stageripenessfruits.Atthesame time，theAFCAAtention Mechanism is introduced inthefeature fusion stage to strengthen global feature expresionand adaptability tocomplex backgrounds，andachieve eficient fruit quality detection andlabeling.Experimental results showthat AGLU-YOLOv1lperforsbeterin precision，robustnessand multi-saleobject adaptability than other detection models inPrecision，Recall， mAP @ 0 . 5 and 1 n A P@ 0 . 5：0 . 9 5 indicators，and can better meet the demands for identifying fruit ripeness.
Keywords：YOLO;ObjectDetection;CGLU;CATM; fruit ripenessdetection
0 引言
社會經(jīng)濟的飛速發(fā)展下，人們的生活水平顯著提高，水果作為人類膳食結(jié)構(gòu)中的重要組成部分，不僅為人體提供豐富的維生素、礦物質(zhì)和膳食纖維，還在促進免疫力和預(yù)防慢性疾病中發(fā)揮著重要作用[]。（剩余9445字）