With the development of artificial intelligence algorithms, the application of big data in various fields is becoming increasingly widespread, including computer vision, speech recognition, intelligent robots, etc. However, there are few applications for robot object recognition systems. This article would combine existing artificial intelligence technologies to study object recognition systems, and upgraded the robot object recognition system using convolutional neural network models and error back propagation models, respectively. Through experiments, it was found that convolutional neural networks could shorten the time to 0.8 seconds, while the error back propagation model could improve the recognition accuracy to 95%, with an average accuracy of 91.7%. The results indicated that the convolutional neural network model had a significant effect in shortening recognition time; while the error back propagation model was effective in improving recognition accuracy.

Lina Gong, Liang Tang. Investigation on Artificial Intelligence Algorithms in Robot Object Recognition Systems under the Background of Big Data. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 7: 32-38. https://doi.org/10.25236/AJCIS.2023.060705.

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