Shelter identification for shelter-transporting AGV based on improved target detection model yolov5

Abstract Shelter identification is the fundamental issue to make shelter-transporting automated guided vehicle (AGV) effectively detect and transport shelter. Actively identifying shelter has an important problem of high accuracy but slow speed for a complex model, and fast speed but low accuracy for a simple model. However, all kinds of target detection algorithms available have low 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 (NMS). 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.