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New CAppleNet model enhances real-time apple landmark detection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CAppleNet, a new model for detecting specific apple landmarks like stem cavities and calyxes in real-time. The model, based on the YOLO11 architecture, was optimized through a systematic study of various factors including input resolution, data augmentation, and architectural enhancements. The most significant improvement came from replacing the C2PSA block with Coordinate Attention (CA), which reduced the model's parameter count by over 10% while achieving a higher mean Average Precision (mAP) and a better F1 score for stem cavity detection. AI

IMPACT This model could improve automated fruit sorting and grading systems by enabling more precise real-time detection of key fruit features.

RANK_REASON The cluster describes a new model and dataset presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CAppleNet model enhances real-time apple landmark detection

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The cluster describes a new model and dataset presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

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

    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…

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