Leveraging Deep Learning and Sensor Fusion Algorithms for Advanced Machine Vision and Perception in IoRT-Enabled Smart Manufacturing

This work focuses on the IoT Robotic Things and machine vision and perception algorithms to improve smart manufacturing in Industry 4.0. This paper exemplifies potential enhancements of IoRT systems by utilizing deep learning and sensor fusion strategies. Hence, through Convolutional Neural Networks (CNNs) for detecting and classifying objects and through image segmentation such as U-Net, the system analyzes large, dense and rich visual data typical of industrial scenarios. Pose estimation and 3D reconstruction are employed to develop highly refined spatial perceptive abilities. Data from multiple sensors is fused using Sensor fusion techniques such as Kalman and Particle Filters; thus improving the situational awareness, for closed loop decision making. Since the analysis happens at the edge side, it happens in real-time with very little latency. Both data augmentation and synthetic data enhance the stability and, in turn, the robustness of the model. This approach not only improves manufacturing processes to increase precision and efficiency but also sets a practical base for future intelligent and self-organized technical systems making IoRT a part of smart manufacturing.