Optimization of Machine Learning-Based Automatic Target Detection and Locking System on Robots

Abstract

Background: In recent years, the world of robotics has made significant progress in improving the operational capabilities of robots through target detection and locking systems. These systems play a crucial role in improving the efficiency and effectiveness of critical applications such as defense, security, and industrial automation. However, the main challenge faced is the limitations of the existing system in adapting to unstable environmental conditions and dynamic changes in targets. Objective: This research aims to overcome these challenges by developing a more adaptive and responsive target detection and locking system by integrating two leading machine learning technologies: Convolutional Neural Networks (CNN) for target detection and Long Short-Term Memory (LSTM) for target tracking. Methods: This study uses a quantitative approach to evaluate the effectiveness of the integration of CNNs and LSTMs in target detection and locking systems. Results: The results of the study showed a detection accuracy rate of 95% and a locking accuracy of 90%. The system is proven to be able to adapt to changing operational conditions in real-time and provide consistent performance in a variety of complex and dynamic scenarios. Conclusion: The conclusion of this study is that the integration of CNN and LSTM technologies in target detection and locking systems in robots significantly improves the performance and efficiency of the system, enabling a wider and more complex application.