Comparing YOLOv8 and Mask RCNN for object segmentation in complex orchard environments
CoRR(2023)
摘要
Instance segmentation, an important image processing operation for automation
in agriculture, is used to precisely delineate individual objects of interest
within images, which provides foundational information for various automated or
robotic tasks such as selective harvesting and precision pruning. This study
compares the one-stage YOLOv8 and the two-stage Mask R-CNN machine learning
models for instance segmentation under varying orchard conditions across two
datasets. Dataset 1, collected in dormant season, includes images of dormant
apple trees, which were used to train multi-object segmentation models
delineating tree branches and trunks. Dataset 2, collected in the early growing
season, includes images of apple tree canopies with green foliage and immature
(green) apples (also called fruitlet), which were used to train single-object
segmentation models delineating only immature green apples. The results showed
that YOLOv8 performed better than Mask R-CNN, achieving good precision and
near-perfect recall across both datasets at a confidence threshold of 0.5.
Specifically, for Dataset 1, YOLOv8 achieved a precision of 0.90 and a recall
of 0.95 for all classes. In comparison, Mask R-CNN demonstrated a precision of
0.81 and a recall of 0.81 for the same dataset. With Dataset 2, YOLOv8 achieved
a precision of 0.93 and a recall of 0.97. Mask R-CNN, in this single-class
scenario, achieved a precision of 0.85 and a recall of 0.88. Additionally, the
inference times for YOLOv8 were 10.9 ms for multi-class segmentation (Dataset
1) and 7.8 ms for single-class segmentation (Dataset 2), compared to 15.6 ms
and 12.8 ms achieved by Mask R-CNN's, respectively.
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