GA-YOLO: A Lightweight YOLO Model for Dense and Occluded Grape Target Detection

• Published in: Horticulturae Vol. 9; no. 4; p. 443
• Main Authors: Chen, Jiqing, Ma, Aoqiang, Huang, Lixiang, Su, Yousheng, Li, Wenqu, Zhang, Hongdu, Wang, Zhikui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Accuracy; Algorithms; Apples; Cameras; China; Computer networks; computer vision; Datasets; Deep learning; dense and occluded target; Digital agriculture; Environmental aspects; Fruits; GA-YOLO; grape detection; Grapes; Labeling; lightweight model; Machine vision; Mathematical models; Methods; Parameters; Physiological aspects; Picking; picking robot; Robots; Rural areas; Support vector machines; Target detection; China

Picking robots have become an important development direction of smart agriculture, and the position detection of fruit is the key to realizing robot picking. However, the existing detection models have the shortcomings of missing detection and slow detection speed when detecting dense and occluded...