Augmentation Method for High Intra-Class Variation Data in Apple Detection

文献类型： 外文期刊

第一作者： Li, Huibin

作者： Li, Huibin;Shi, Yun;Guo, Wei;Lu, Guowen

作者机构：

关键词： deep learning; modern orchards; visual detection; fruit picking; lightweight detection models; augmentation method

期刊名称：SENSORS （ 影响因子：3.847； 五年影响因子：4.05 ）

ISSN：

年卷期： 2022 年 22 卷 17 期

页码：

收录情况： SCI

摘要： Deep learning is widely used in modern orchard production for various inspection missions, which helps improve the efficiency of orchard operations. In the mission of visual detection during fruit picking, most current lightweight detection models are not yet effective enough to detect multi-type occlusion targets, severely affecting automated fruit-picking efficiency. This study addresses this problem by proposing the pioneering design of a multi-type occlusion apple dataset and an augmentation method of data balance. We divided apple occlusion into eight types and used the proposed method to balance the number of annotation boxes for multi-type occlusion apple targets. Finally, a validation experiment was carried out using five popular lightweight object detection models: yolox-s, yolov5-s, yolov4-s, yolov3-tiny, and efficidentdet-d0. The results show that, using the proposed augmentation method, the average detection precision of the five popular lightweight object detection models improved significantly. Specifically, the precision increased from 0.894 to 0.974, recall increased from 0.845 to 0.972, and mAP0.5 increased from 0.982 to 0.919 for yolox-s. This implies that the proposed augmentation method shows great potential for different fruit detection missions in future orchard applications.

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