Industrial 4.0 targets the increased automation for production processes within the manufacturing industry, including smart factories, smart manufacture, and so on. Industrial 5.0 emphasizes the human-centric collaboration between human and robots with artificial intelligence, that is, collaborative robot (cobot), to optimize the manufacturing process with improved automation. Machine vision is one of key technologies in Industrial 4.0 and 5.0 and the real-time processing of visual data at the edge is crucial for machine vision. The main use case of machine vision is visual quality inspection, where 2D or 3D vision-based DL is used for various purposes, for example, verifying the presence or absence of parts or ingredients in packaging systems, detecting defects, or identifying the characters on printed circuit board (PCB), gauging the dimension of parts, verifying proper assembly of parts, and the wrapping of labels around containers, detecting tool wear defects as preventive maintenance, and UAV- or drone-based fault detection systems of solar panels, turbines and pipeline, and so forth. The robot arm for pick and place of parts and assembly is another use case of machine vision for the improved collaboration between human and cobots.

Figure 3-2 Machine Vision Block Diagram With Data Flow on AM68A

Figure 3-2 illustrates the data flow for a machine vision use case example on the AM68A, which involves capturing an image sequence at 30 fps using an 8MP camera through a MIPI CSI-2 RX port. The captured raw Bayer image is processed and demosaiced to YUV by VPAC3 VISS, and VPAC3 LDC corrects any lens distortion that can be present. In this machine vision use case, the DL networks are applied to regions of interest (ROI), which are extracted on A72 cores. The number of ROIs and their sizes vary based on the specific use case. The frame rate at which DL networks are applied is also dependent on the use case. The output obtained through DL preprocessing, DL network on MMA, and DL post-processing is displayed via DSS. In the event of any unexpected detection, an alarm can be activated for human attention. The resource utilization and estimated power consumption of AM68A are shown in Table 3-2 for this machine vision use case with a single 8MP input. MMA is assumed to be fully utilized even though the actual MMA utilization can depend on the application. There is still enough room for CSI-2, VPAC, A72, and DDR bandwidth to process higher resolutions of input, for example, 1 × 16MP at 30 fps or another input of 8MP, for example, 2 × 8MP at 30 fps. Therefore, the AM68A can enable the machine vision use case for these camera configurations as long as the MMA can handle the necessary DL inferrencing, but at the cost of increased power.