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Accurate Fruit and Vegetable Detection Method for Space Station Cargo Bay[J]. Chinese Journal of Space Science. doi: 10.11728/cjss2025-0080
Citation: Accurate Fruit and Vegetable Detection Method for Space Station Cargo Bay[J]. Chinese Journal of Space Science. doi: 10.11728/cjss2025-0080
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Accurate Fruit and Vegetable Detection Method for Space Station Cargo Bay

doi: 10.11728/cjss2025-0080
  • Received Date: 2025-05-16
  • Accepted Date: 2025-09-10
  • Rev Recd Date: 2025-09-04
    • Available Online: 2025-11-27
    Abstract
    1. The practical application of machine vision technology in the management of space station cargo bay is in the preliminary stage, in order to solve the lack of detection accuracy due to the narrow space, occlusion and light problems in the space station cargo bay, an improved algorithm for the detection of fruits and vegetables in the space station cargo bay based on YOLO11 is proposed: LEBR-YOLO. the method improves convolution to an efficient input feature extraction stem layer combining spatial information and edge information. A two-layer attention mechanism is added to improve the feature extraction capability. An improved lightweight shared deformable detection module is introduced to improve the detection ability under occlusion. Migration learning is also used as a method to optimize the model to compensate for the lack of dataset and improve the generalization ability. Experiments show that the method achieves 95.3% accuracy, 88.6% recall and 93.9% mAP@0.5 on the homemade fruit and vegetable dataset, while still maintaining a low model complexity to meet the needs of the Lightship cargo spacecraft in orbit. The method can be effectively used for the detection of fruit and vegetable items in the space station, which improves the detection accuracy and effectively reduces misdetection and omission.

     

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    通讯作者: 陈斌, bchen63@163.com
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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