Abstract
Target recognition technology is an important topic in the field of artificial intelligence, which is widely used in fields such as medicine, robot vision, highway traffic, and VR technology. In the robot vision system, the target recognition technology requires high precision, real-time, and practicality, and it must be able to cope with the harsh recognition environment on site. This paper mainly studies the target recognition method of rehabilitation robot based on image local features. In this paper, a rehabilitation robot recognition system is established based on the local features of the image and the target recognition technology. Firstly, after collecting or processing the initially prepared images, feature extraction is performed, and the feature vectors extracted from the features are classified and recognized and then transferred to the recognition model of the rehabilitation robot. Then, according to the functional requirements of the rehabilitation robot recognition system, the “one-to-many” rehabilitation mode and real-time system monitoring are realized to accurately identify the target state. Finally, the Harris algorithm is used to convert the predetermined image set into a grayscale image, and the Gaussian distribution is used uniformly to determine the location and proportion of the feature points, and extract the corresponding image. The Harris matching method used in this article is very effective and has the highest accuracy. The number of correct matches accounted for 58.3%. Compared with the SIFT algorithm and the Harris+SIFT algorithm, the accuracy rate increases by 12.3% and 14.4%, respectively. The recall rate increased by 13.2% and 8.9% respectively. The experimental results show that the target recognition technology of rehabilitation robot based on image local features is more accurate than general technology.
Highlights
With the advent of the ‘‘artificial intelligence’’ era, more and more intelligent robots appear in daily life [1]
It is necessary for intelligent robots to have the same sensory capabilities as humans
Compared with the SIFT algorithm and the Harris+SIFT algorithm, the accuracy is improved by 12.3. % And 14.4%, the recall rate increased by 13.2% and 8.9% respectively
Summary
With the advent of the ‘‘artificial intelligence’’ era, more and more intelligent robots appear in daily life [1]. Wu: Target Recognition Method of Rehabilitation Robot Based on Image Local Features. Aiming at the problems of slow training speed of high-dimensional image information input network and low information utilization rate, the feature selection is performed to reduce the image dimension and simplify the image information, and extract useful feature information that can better reflect the essence of the target image, and improve Network recognition rate. For the target graphics that have been input into the system and the scattered building blocks in the work platform, the system needs to be able to recognize the different types of blocks contained in the target graphics during the process of assisted rehabilitation, and according to the actual work platform needs to use the location of the blocks And related information such as phase angle, generate the corresponding puzzle strategy. The system should be equipped to automatically operate the robotic arm to replace the medical staff, and follow the corresponding strategy to complete the teaching for the corresponding tasks of the patient [43]
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