Relative Position Detection of Clustered Tomatoes Based on BlendMask-BiFPN

Authors

  • Caiping Guo Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University (East Campus), Beijing, China
  • Can Tang Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University (East Campus), Beijing, China
  • Yehong Liu Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University (East Campus), Beijing, China
  • Xin Wang Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University (East Campus), Beijing, China
  • Shumao Wang Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University (East Campus), Beijing, China

DOI:

https://doi.org/10.5755/j02.eie.38247

Keywords:

Automation; BlendMask-BiFPN; Deep learning; Neural networks; Robot; Tomato harvesting

Abstract

In robotic harvesting, maneuvering around obstacles to position manipulators is challenging, especially in unstructured environments. This study proposes a method to detect the relative position of tomato bunches to the main stem position using the BlendMask-BiFPN algorithm. Initial comparative tests between full-stem and partial-stem labelling strategies revealed that the latter produced more complete peduncle masks, which guided our choice for subsequent experiments. Significant modifications to the BlendMask algorithm included the integration of a ResNet-101-BiFPN backbone, which improved the feature fusion network of the model. The revised model demonstrated high efficiency in pinpointing the relative positions of clustered tomatoes, achieving 91.3 % ARmask 50 and 84.8 % APmask 50 for the detection of tomato bunches. Comparisons with Mask RCNN, YOLACT, YOLACT++, and YOLOv8 showed that the BlendMask-BiFPN model outperforms these alternatives, suggesting its potential for more effective robotic harvesting in complex agricultural scenarios.

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Published

2024-08-26

How to Cite

Guo, C., Tang, C., Liu, Y., Wang, X., & Wang, S. (2024). Relative Position Detection of Clustered Tomatoes Based on BlendMask-BiFPN. Elektronika Ir Elektrotechnika, 30(4), 52-60. https://doi.org/10.5755/j02.eie.38247

Issue

Vol. 30 No. 4 (2024)

Section

SYSTEM ENGINEERING, COMPUTER TECHNOLOGY

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