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Fig-YOLO: An Improved YOLOv11-Based Fig Detection Algorithm for Complex Environments
DOI:10.3390/foods14234154.png)
摘要
En
Keyword:
fig detection
Fig-YOLO
YOLOv11n
complex environments
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期刊
IF:
5.1
论文数:
8.1K
被引数:
8.7W
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引用论文
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks更快的r-cnn: 基于区域建议网络的实时目标检测
YOLOv8n-Pose-DSW: A Precision Picking Point Localization Model for Zucchini in Complex Greenhouse EnvironmentsYOLOv8n-Pose-DSW:一种用于复杂温室环境下西葫芦精确采摘点定位模型
Wang, L.; Wang, S.; Wang, B.; Yang, Z.; Zhang, Y. Jujube-YOLO: A precise jujube fruit recognition model in unstructured environments. Expert Syst. Appl. 2025, 291, 128530. [Google Scholar] [CrossRef]王, L.; 王, S.; 王, B.; 杨, Z.; 张, Y. Jujube-YOLO: 一种在非结构化环境中精确识别枣果的模型。Expert Syst. Appl. 2025, 291, 128530. [Google Scholar] [CrossRef]
A Robust Multi-label Fruit Classification Based on Deep Convolution Neural Network基于深度卷积神经网络的鲁棒多标签水果分类
The fig (
Ficus carica
L.): varietal evolution from Asia to Puglia region, southeastern Italy无花果(Ficus carica L.):从亚洲到意大利东南部普利亚地区的品种演化
Detection and picking point localization of grape bunches and stems based on oriented bounding box基于定向边界框的葡萄串和茎的检测与拾取点定位

