Research on Multi-channel Vision Platform for Intelligent Robots Based on Deep Learning

In recent years, the application of intelligent robots on multi-channel visual platforms has received widespread attention, especially with the integration of deep learning technology, significantly enhancing their perception and decision-making capabilities in complex environments. This study focuses on exploring the application and optimization of deep learning-based object tracking algorithms in intelligent robots’ multi-channel visual platforms. Firstly, it elaborates on the basic concepts of deep learning and its application in object tracking, including the structure of Convolutional Neural Networks (CNN) and several typical tracking algorithms such as FCNT,HCF, and MDNet. Next, it deeply analyzes the implementation principles and network structure of the real-time tracking algorithm GOTURN based on the Caffe framework. Furthermore, the paper designs and implements a multi-channel visual platform, including detailed design schemes for both hardware and software aspects. The effectiveness of the proposed object tracking algorithm is verified through experiments, and a thorough analysis of its performance metrics is conducted. The research indicates that the application of deep learning-based object tracking algorithms on multi-channel visual platforms can significantly enhance the visual perception capabilities of intelligent robots, providing new ideas and methods for the future development of intelligent robot technology.