Research on detection and recognition method of staircase image based on YOLOv5s
Stairway area is a typical environmental target.Whether it is a robot system that can climb stairs autonomously or a software system that can alert the visually impaired to obstacles,it needs to have the function of detecting and recognizing stairs.To help them navigate their surroundings,a stair case image detection algorithm based on YOLOv5s was designed.Firstly,Labelme was used to annotate the stair data set,and the format of the an-notated file was transformed.Secondly,the YOLOv5s network model training environment was built,the pre-trained model configuration file was modified,the model weight data migration training was started,and the optimal model parameters were output.Finally,the test data set was loaded to test the effect iveness of the trained optimal model algorithm.The algorithms can recognize the up and down stairs image,and through the comparison test with other target detection algorithms,it has a higher recognition accuracy.The results showed that the mean accuracy of the algorithm reached 80.3%,and the generalization ability was strong.It is feasible to apply the algorithm to the detec-tion method of stairway up and down stairs,which can provide some references for the automatic detection and recognition of stairs by robots,and can also provide effective help for the visually impaired.The market application prospect is good.
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