Fruit recognition based on occlusion enhanced knowledge distillation

SHI Yuanping; MA Yanheng; GENG Liang; CHU Lina; LI Wei; LI Bingxuan

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Fruit recognition based on occlusion enhanced knowledge distillation

Optical Measurement & Perception | 更新时间：2025-09-22

- Fruit recognition based on occlusion enhanced knowledge distillation
- OPTICAL TECHNIQUE Vol. 51, Issue 5, Pages: 617-625(2025)
- 作者机构：
  
  1.陆军工程大学石家庄校区，河北石家庄 050003
  2.石家庄学院机电工程学院，河北石家庄 050035
  3.石家庄市农业机器人智能感知重点实验室，河北石家庄 050035
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Received：17 June 2025，
  
  Revised：2025-07-06，
  
  Published：20 September 2025
- 稿件说明：
移动端阅览
史源平,马彦恒,耿亮等.基于遮挡增强知识蒸馏的果实识别[J].光学技术,2025,51(05):617-625.

SHI Yuanping,MA Yanheng,GENG Liang,et al.Fruit recognition based on occlusion enhanced knowledge distillation[J].OPTICAL TECHNIQUE,2025,51(05):617-625.
史源平,马彦恒,耿亮等.基于遮挡增强知识蒸馏的果实识别[J].光学技术,2025,51(05):617-625. DOI：

SHI Yuanping,MA Yanheng,GENG Liang,et al.Fruit recognition based on occlusion enhanced knowledge distillation[J].OPTICAL TECHNIQUE,2025,51(05):617-625. DOI：

摘要

果实被枝叶遮挡时会显著削弱采摘机器人视觉系统的识别精度。针对这一问题，文章提出一种基于“遮挡增强知识蒸馏”的果实识别框架。该框架在构建的遮挡数据集上，通过引入遮挡信息约束多尺度语义特征对齐，并结合指数移动平均策略稳定训练过程。在MinneApple数据集上的实验结果表明，AP@[0.5:0.05:0.95]、AP@0.5、AP@0.75、小目标AP与大目标AP分别达到0.744、0.946、0.793、0.674和0.976，较主流检测模型平均提升0.178。研究证明，方法能够在复杂遮挡场景下显著提高采摘机器人的视觉感知精度，为智慧农业果实自动采摘提供有效技术支撑。

Abstract

In order to solve the problem that the fruit covered by branches and leaves will affect the optical recognition effect of the picking robot

a recognition method of "shading enhanced distillation" is proposed. On the self-made occlusion data set

the method uses occlusion information to standardize the learning of semantic alignment features

and uses exponential moving average to enhance the training stability. Experiments are carried out on the MinneApple dataset. The AP@[0.5:0.05:0.95] is 0.744. The AP@0.5 is 0.946. The AP@0.75 is 0.793. The AP for small objects is 0.674. The AP for large objects is 0.976. These results show an average improvement of 0.178 over mainstream detection models. The experimental results show that this method can provide effective technical support for the accurate visual perception of picking robot in occluded scenes.

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references

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