Fast and stable pedicel detection for robust visual servoing to harvest shaking fruits

Park, Yonghyun; Kim, Changjo; Son, Hyoung Il

doi:10.1016/j.compag.2024.108863

Cited by 4 publications

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Revolutionizing automated pear picking using Mamba architecture

Zhao,

Cai,

Zhou

et al. 2024

Plant Methods

View full text Add to dashboard Cite

With the emergence of the new generation vision architecture Vmamba and the further demand for agricultural yield and efficiency, we propose an efficient and high-accuracy target detection network for automated pear picking tasks based on Vmamba, aiming to address the issue of low efficiency in current Transformer architectures. The proposed network, named SRSMamba, employs a Reward and Punishment Mechanism (RPM) to focus on important information while minimizing redundancy interference. It utilizes 3D Selective Scan (SS3D) to extend scanning dimensions and integrates global information across channel dimensions, thereby enhancing the model's robustness in complex agricultural environments and effectively adapting to the extraction of complex features in pear orchards and farmlands. Additionally, a Stacked Feature Pyramid Network (SFPN) is introduced to enhance semantic information during the feature fusion stage, particularly improving the detection capability for small targets. Experimental results show that SRSMamba has a low parameter count of 21.1 M, GFLOPs of 50.4, mAP of 72.0%, mAP50 reaching 94.8%, mAP75 at 68.1%, and FPS at 26.9. Compared with other state-of-the-art (SOTA) object detection methods, it achieves a good trade-off between model efficiency and detection accuracy.

show abstract