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

ABSTRACT: 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

SUBMITTER: Li H 

PROVIDER: S-EPMC9460715 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature