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Shelter identification is the fundamental issue for the shelter-transporting automated guided vehicle to detect and transport shelter effectively. Actively identifying shelter faces the challenge of high accuracy but slow speed using a complex model, and fast speed but low accuracy using a simple model. However, all kinds of target detection algorithms available has difficulty in achieving both high detection accuracy and speed. In this paper, the model YOLOv5n6&#x002A; is developed based on the modified YOLOv5 model by selecting different model structures, introducing an attention mechanism, and improving loss function and non-maximum suppression. Then, the experiments for shelter recognition were carried out using the model YOLOv5n6&#x002A;. The experimental results show that the box&#x005F;loss is reduced by 1.2&#x0025;, the mAP&#x005F;0.5:0.95 is improved by 2&#x0025;, and the detection accuracy is improved by 0.87&#x0025; for the improved model YOLOv5n6&#x002A; compared with the YOLOv5n6. However, the YOLOv5n6&#x002A; size is only 7.2M, and the detection time is increased by 0.2ms. So it is proved that the modified model YOLOv5n6&#x002A; not only has a significant improvement in the shelter detection ability but also has strong robustness, which meets both the requirements of the recognition accuracy and the detection speed.

Shelter Identification for Shelter-Transporting AGV Based on Improved Target Detection Model YOLOv5