Open Access iconOpen Access

ARTICLE

crossmark

Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

Chengjun Wang1,2, Fan Ding2,*, Yiwen Wang1, Renyuan Wu1, Xingyu Yao2, Chengjie Jiang1, Liuyi Ling1

1 School of Artificial Intelligence, Anhui University of Science and Technology, Huainan, 232001, China
2 School of Mechanical Engineering, Anhui University of Science and Technology, Huainan, 232001, China

* Corresponding Author: Fan Ding. Email: email

(This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)

Computers, Materials & Continua 2024, 78(1), 1481-1501. https://doi.org/10.32604/cmc.2023.046876

Abstract

The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots. Real-time identification of strawberries in an unstructured environment is a challenging task. Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy. To this end, the present study proposes an Efficient YOLACT (E-YOLACT) algorithm for strawberry detection and segmentation based on the YOLACT framework. The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism, pyramid squeeze shuffle attention (PSSA), for efficient feature extraction. Additionally, an attention-guided context-feature pyramid network (AC-FPN) is employed instead of FPN to optimize the architecture’s performance. Furthermore, a feature-enhanced model (FEM) is introduced to enhance the prediction head’s capabilities, while efficient fast non-maximum suppression (EF-NMS) is devised to improve non-maximum suppression. The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6, respectively, on the custom dataset. Moreover, it exhibits an impressive category accuracy of 93.5%. Notably, the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS. The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.

Keywords


Cite This Article

APA Style
Wang, C., Ding, F., Wang, Y., Wu, R., Yao, X. et al. (2024). Real-time detection and instance segmentation of strawberry in unstructured environment. Computers, Materials & Continua, 78(1), 1481-1501. https://doi.org/10.32604/cmc.2023.046876
Vancouver Style
Wang C, Ding F, Wang Y, Wu R, Yao X, Jiang C, et al. Real-time detection and instance segmentation of strawberry in unstructured environment. Comput Mater Contin. 2024;78(1):1481-1501 https://doi.org/10.32604/cmc.2023.046876
IEEE Style
C. Wang et al., “Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment,” Comput. Mater. Contin., vol. 78, no. 1, pp. 1481-1501, 2024. https://doi.org/10.32604/cmc.2023.046876



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 635

    View

  • 258

    Download

  • 0

    Like

Share Link