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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.07935 |
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| _version_ | 1866911189598994432 |
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| author | Sapkota, Ranjan Ahmed, Dawood Karkee, Manoj |
| author_facet | Sapkota, Ranjan Ahmed, Dawood Karkee, Manoj |
| contents | Instance segmentation is an important image processing operation for agricultural automation, providing precise delineation of individual objects within images and enabling tasks such as selective harvesting and precision pruning. This study compares the one stage YOLOv8 model with the two stage Mask R CNN model for instance segmentation under varying orchard conditions across two datasets. Dataset 1, collected in the dormant season, contains images of apple trees without foliage and was used to train multi object segmentation models delineating branches and trunks. Dataset 2, collected in the early growing season, includes canopy images with green foliage and immature apples and was used to train single object segmentation models delineating fruitlets. Results showed YOLOv8 outperformed Mask R CNN with higher precision and near perfect recall at a confidence threshold of 0.5. For Dataset 1, YOLOv8 achieved precision 0.90 and recall 0.95 compared to 0.81 and 0.81 for Mask R CNN. For Dataset 2, YOLOv8 reached precision 0.93 and recall 0.97 compared to 0.85 and 0.88. Inference times were also lower for YOLOv8, at 10.9 ms and 7.8 ms, versus 15.6 ms and 12.8 ms for Mask R CNN. These findings demonstrate superior accuracy and efficiency of YOLOv8 for real time orchard automation tasks such as robotic harvesting and fruit thinning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_07935 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments Sapkota, Ranjan Ahmed, Dawood Karkee, Manoj Computer Vision and Pattern Recognition Instance segmentation is an important image processing operation for agricultural automation, providing precise delineation of individual objects within images and enabling tasks such as selective harvesting and precision pruning. This study compares the one stage YOLOv8 model with the two stage Mask R CNN model for instance segmentation under varying orchard conditions across two datasets. Dataset 1, collected in the dormant season, contains images of apple trees without foliage and was used to train multi object segmentation models delineating branches and trunks. Dataset 2, collected in the early growing season, includes canopy images with green foliage and immature apples and was used to train single object segmentation models delineating fruitlets. Results showed YOLOv8 outperformed Mask R CNN with higher precision and near perfect recall at a confidence threshold of 0.5. For Dataset 1, YOLOv8 achieved precision 0.90 and recall 0.95 compared to 0.81 and 0.81 for Mask R CNN. For Dataset 2, YOLOv8 reached precision 0.93 and recall 0.97 compared to 0.85 and 0.88. Inference times were also lower for YOLOv8, at 10.9 ms and 7.8 ms, versus 15.6 ms and 12.8 ms for Mask R CNN. These findings demonstrate superior accuracy and efficiency of YOLOv8 for real time orchard automation tasks such as robotic harvesting and fruit thinning. |
| title | Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.07935 |