Visual Image Feature Recognition Method for Mobile Robots Based on Machine Vision

Abstract

With the continuous advancement of machine vision and computer technology, mobile robots with visual systems have received widespread attention in fields such as industry, agriculture, and services. However, the current methods for processing visual images of mobile robots are difficult to meet the requirements of practical applications. There are issues of low efficiency and low accuracy. Therefore, firstly, spatial information is integrated into the K-means algorithm and image spatial structure constraints are introduced for visual image segmentation. Then the dense connected network is added to the Convolutional neural network structure. This structure is combined with a bidirectional long-term and short-term memory network to achieve visual image feature recognition. The results show that the improved K-means algorithm has a maximum recall rate of 97.35% in the Berkeley image segmentation dataset, with a maximum Randall index of 86.18%. After combining with the proposed improved Convolutional neural network, the highest feature recognition rate for five scenes of mining, risk elimination, agriculture, factory and building is 96.1%, and the lowest error rate is 1.2%. It possesses a high degree of recognition accuracy and is capable of effectively being applied to visual feature recognition on mobile robots, providing a novel reference point for visual image processing on mobile robots.