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

Abstract

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∗ 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∗. The experimental results show that the box-loss is reduced by 1.2%, the mAP-0.5:0.95 is improved by 2%, and the detection accuracy is improved by 0.87% for the improved model YOLOv5n6∗ compared with the YOLOv5n6. However, the YOLOv5n6∗ size is only 7.2M, and the detection time is increased by 0.2ms. So it is proved that the modified model YOLOv5n6∗ 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.