A staircase recognition algorithm based on improved YOLOv5 is proposed for staircase target recognition during autonomous climbing of intelligent metamorphic vehicles in indoor staircase envi-ronment.In order to meet the real-time requirements of the model,the lightweight network Efficient-NetV2 is used to replace the backbone network of the YOLOv5 algorithm.Then,the GSConv module and the VoV-GSCSP module are used to replace the Conv module and the CSP module in the original neck network to further reduce the computational cost based on the enhanced target feature response.To compensate for the loss of accuracy due to model simplification,a coordinate attention mechanism is added to the neck network to enhance target recognition in complex scenes by strengthening target attention.Finally,the improved model is applied to the embedded platform,and the experimental re-sults show that the average detection accuracy of the improved model is 91.99％and the model size is only 3.1 MB,which has obvious superiority compared with other target detection algorithms.This al-gorithm can effectively detect stairs in real time and accurately,which lays a theoretical foundation for the subsequent process of autonomous obstacle crossing of the metamorphic vehicle.
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