Segmentation Method of Zanthoxylum bungeanum Cluster Based on Improved Mask R-CNN

Zhiyong Zhang (), Shuo Wang, Chen Wang, Li Wang, Yanqing Zhang and Haiyan Song
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Zhiyong Zhang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Shuo Wang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Chen Wang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Li Wang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Yanqing Zhang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Haiyan Song: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China

Agriculture, 2024, vol. 14, issue 9, 1-15

Abstract: The precise segmentation of Zanthoxylum bungeanum clusters is crucial for developing picking robots. An improved Mask R-CNN model was proposed in this study for the segmentation of Zanthoxylum bungeanum clusters in natural environments. Firstly, the Swin-Transformer network was introduced into the model’s backbone as the feature extraction network to enhance the model’s feature extraction capabilities. Then, the SK attention mechanism was utilized to fuse the detailed information into the mask branch from the low-level feature map of the feature pyramid network (FPN), aiming to supplement the image detail features. Finally, the distance intersection over union (DIOU) loss function was adopted to replace the original bounding box loss function of Mask R-CNN. The model was trained and tested based on a self-constructed Zanthoxylum bungeanum cluster dataset. Experiments showed that the improved Mask R-CNN model achieved 84.0% and 77.2% in detection mAP 50 box and segmentation mAP 50 mask , respectively, representing a 5.8% and 4.6% improvement over the baseline Mask R-CNN model. In comparison to conventional instance segmentation models, such as YOLACT, Mask Scoring R-CNN, and SOLOv2, the improved Mask R-CNN model also exhibited higher segmentation precision. This study can provide valuable technology support for the development of Zanthoxylum bungeanum picking robots.

Keywords: Zanthoxylum bungeanum; Mask R-CNN; swin-transformer; attention mechanism (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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