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 self-annotated dataset of 1506 apple images (1054 training, 301 validation, 151 test) captured under controlled, fixed-camera conditions and annotated for three target classes: fruit body, stem cavity, and calyx. A YOLO11s detector trained with default hyperparameters is adopted as the baseline, and a systematic, one-fac

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