An Accurate Detection Method for Fruits and Vegetables in the Space Station Cargo Bay

Machine vision technology remains in its nascent stage for practical application in space station cargo bay management, with limited research currently addressing target detection in space station environments. To overcome detection accuracy limitations caused by confined spaces, obstructions, and lighting conditions in cargo bays, this study proposes an enhanced YOLOv11-based algorithm for fruit and vegetable detection: LEBR-YOLO. Drawing on successful implementations of existing visual detection techniques, this approach refines the original convolutional neural network architecture by integrating spatial and edge information via a dual-layer attention mechanism, thereby enhancing processing efficiency for high-resolution feature maps. Specifically, it improves feature extraction capabilities by modifying the original convolutional module into an efficient input feature extraction layer that fuses spatial and edge information. Concurrently, a dual-layer attention mechanism is incorporated to significantly boost the model’s processing efficiency for high-resolution feature maps. An enhanced lightweight shared deformable detection module is introduced, which adopts a shared convolutional architecture combined with deformable convolutions; a dynamic adjustment mechanism integrating category loss and bounding box loss is also employed to improve detection performance under occlusion. Transfer learning is used as an optimization technique to compensate for dataset limitations, reducing computational costs while enhancing model generalization. Experiments demonstrate that this model significantly improves object detection under occlusion: on a custom fruit and vegetable dataset, it achieves 95.3% accuracy, 88.6% recall, and 93.9% mAP@0.5, while maintaining low model complexity. This meets the detection requirements for the Tianzhou cargo spacecraft during in-orbit operations. This approach proves highly effective for detecting fruits and vegetables in space stations, enhancing detection accuracy, substantially reducing false positives and false negatives, and elevating the automation level of on-board resource management.