English(EN) CAppleNet: A Coordinate Attention-Based YOLO11 for Real-Time Apple Stem Cavity and Calyx Detection

新的CAppleNet模型增强了实时苹果地标检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了CAppleNet，一种用于实时检测特定苹果地标（如萼洼和花萼）的新模型。该模型基于YOLO11架构，通过对输入分辨率、数据增强和架构增强等各种因素的系统研究进行了优化。最显著的改进来自于用坐标注意力（CA）替换C2PSA块，这使得模型参数数量减少了10%以上，同时在萼洼检测方面实现了更高的平均精度均值（mAP）和更好的F1分数。 AI

影响该模型通过实现对关键水果特征的更精确的实时检测，有望改进自动化水果分拣和分级系统。

排序理由该集群描述了学术论文中提出的新模型和数据集。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Alican G\"une\c{s}, Robert Selim Hill, G\"okhan Hasano\u{g}lu, Murat Kurt · 2026-11-11 05:00

CAppleNet：基于坐标注意力机制的YOLO11用于实时苹果萼洼和花萼检测

arXiv:2611.13920v1 Announce Type: cross Abstract: Automated fruit grading, sorting, and orientation systems require real-time, accurate detection of fruit-level morphological landmarks rather than the fruit boundary alone. This paper presents a custom, fully self-collected and se…

本文如何被排名

报道来源 [1]

CAppleNet：基于坐标注意力机制的YOLO11用于实时苹果萼洼和花萼检测

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