The Application of Puzhi in Machine Vision Scenarios

In various fields such as industrial automation, intelligent security, and logistics warehousing, machine vision plays a key role by quickly and accurately acquiring image information and processing it for analysis, providing strong support for decision-making. The Puzhi MPSOC development board, with its excellent parallel processing capabilities, high flexibility, and low latency characteristics, has become an ideal choice for building efficient machine vision systems. The machine vision solution based on the Puzhi MPSOC development board fully integrates the advantages of hardware and software, aiming to provide high-performance, customized machine vision solutions for various application scenarios, helping enterprises improve production efficiency, optimize product quality, and enhance security monitoring capabilities.

1. Puzhi MPSOC Development Board: Choose the Puzhi development board equipped with Xilinx UltraScale + series FPGA chips, such as the MPSOC 19EG development board. This series of chips has rich logic resources and high-speed transceivers, enabling large-scale parallel computing to meet the high demand for computing resources of complex algorithms in machine vision. At the same time, it has multiple high-speed interfaces, such as PCIe Gen3 x8, facilitating high-speed data transmission with the host or other devices, ensuring rapid interaction of image data; it also integrates high-speed SerDes interfaces for easy connection to high-resolution image sensors, supporting various image data formats.

2. Image Acquisition Devices: Select suitable image sensors based on the precision and frame rate requirements of different application scenarios. In industrial inspection scenarios, if high-precision detection of small defects is required, the Basler ace acA2040 - 90um camera can be selected, with a resolution of up to 2048×1088 pixels and a frame rate of 90fps, connected to the FPGA development board via Camera Link interface, ensuring that the captured images are clear and accurate, meeting the stringent requirements for detail in industrial inspection; in the intelligent security field, for large scene monitoring, the Hikvision DS - 2CD3T47WD - L camera can be selected, supporting 4 million pixel high-definition imaging, with a frame rate of 25fps, transmitting image data via RJ45 Ethernet interface, adapting to the security monitoring needs for coverage and real-time performance.

3. Storage Devices: Equipped with large-capacity DDR4 memory, with a frequency of 3200MHz, for caching image data and intermediate calculation results, improving data read and write speeds, ensuring smooth operation of machine vision algorithms. At the same time, paired with high-speed solid-state drives (SSD) using NVMe protocol, with sequential read speeds exceeding 3000MB/s and sequential write speeds exceeding 2000MB/s, for storing large amounts of training data and model parameters, ensuring quick access and persistent storage of data, providing data support for the stable operation of the machine vision system.

1. Image Preprocessing Algorithms: Develop a series of image preprocessing algorithms optimized for different application scenarios. In industrial inspection, use median filtering algorithms to remove salt-and-pepper noise from images, enhance image contrast through histogram equalization, and improve image clarity for subsequent feature extraction and defect detection; in intelligent security scenarios, use Gaussian filtering to smooth images, suppress high-frequency noise in images, and improve image stability, providing high-quality image data for target detection and behavior analysis.

2. Implementation of Machine Vision Algorithms: Based on development tools such as OpenCV and Vitis AI, port classic machine vision algorithms and deep learning algorithms to FPGA. For traditional edge detection algorithms, such as the Canny algorithm, utilize the parallel processing capabilities of FPGA to perform parallel calculations on each pixel of the image, quickly and accurately extracting image edge features; in terms of deep learning algorithms, for target detection tasks, adopt the optimized YOLOv5 algorithm, achieving rapid recognition and localization of target objects in images through hardware acceleration. Use Xilinx's Vitis AI tools for quantization and compilation, efficiently mapping deep learning models to FPGA hardware resources, fully leveraging the parallel computing advantages of FPGA to improve detection speed and accuracy.

3. Driver and Interface Software: Develop driver programs for the FPGA development board to communicate with image acquisition devices, storage devices, and the host. Write dedicated driver programs for the Camera Link interface to precisely control interface timing, ensuring stable transmission of image data; for the PCIe interface, develop driver programs based on DMA (Direct Memory Access) technology to achieve high-speed data exchange between FPGA and the host, reducing CPU load and improving overall system performance. At the same time, develop communication interface software with the upper computer or other systems, supporting various communication protocols such as TCP/IP, UDP, etc., facilitating data sharing and collaborative work with other devices.

4. System Management Software: Develop comprehensive system management software for configuring and monitoring the operational status of the machine vision system. Users can set FPGA working parameters such as clock frequency and voltage through this software, flexibly load and update machine vision algorithms and models according to different application scenarios. The software has real-time monitoring capabilities, allowing real-time monitoring of key indicators such as FPGA temperature, power consumption, and resource utilization; in case of any abnormal situation, it immediately issues an alarm to ensure stable and reliable operation of the system. It supports remote management functions, allowing users to connect to the machine vision system remotely via the network for parameter adjustments, algorithm updates, and fault diagnosis, improving system maintenance efficiency.

1. Industrial Automation: Used for product quality inspection and size measurement on industrial production lines. The machine vision system analyzes product images captured by industrial cameras, using edge detection, image segmentation, and other algorithms to quickly and accurately detect whether products have defects such as scratches, cracks, or holes; using size measurement algorithms to accurately measure key dimensions of products, comparing them with preset standards to achieve online monitoring of product quality and quality traceability, improving production efficiency and product quality. For example, in the production of electronic components, detect the soldering quality and size accuracy of chip pins to ensure that products meet quality standards.

2. Intelligent Security: In the field of security monitoring, achieve target detection, behavior analysis, and facial recognition. Real-time processing of video images captured by surveillance cameras, using target detection algorithms such as the YOLO series to quickly identify target objects such as people and vehicles, and analyze their behavior to determine if there are any abnormal behaviors such as loitering, running, or intruding into restricted areas; through facial recognition algorithms, quickly verify personnel identity, comparing recognition results with information in the database for access control, personnel tracking, etc., improving the intelligence level of security monitoring and ensuring the safety of personnel and property.

3. Logistics Warehousing: Used for cargo identification, inventory management, and robot navigation in logistics warehousing scenarios. The machine vision system identifies the appearance and labels of goods, achieving rapid classification and inventory management of goods; using visual SLAM (Simultaneous Localization and Mapping) algorithms to provide environmental perception and navigation information for logistics robots, enabling them to navigate autonomously in warehouses and complete cargo handling tasks; at the same time, by monitoring images of warehouse shelves, real-time updates of inventory information are achieved, realizing intelligent management of inventory and improving the efficiency and accuracy of logistics warehousing.

4. Agricultural Field: Used for crop growth monitoring, pest detection, and fruit picking in agricultural production. By analyzing images captured by cameras in farmland, using image recognition and deep learning algorithms to monitor crop growth conditions such as plant height, leaf area index, and nutritional status; detecting whether crops are affected by pests, timely discovering early symptoms of pest damage, and taking corresponding preventive measures; in the fruit picking stage, using machine vision to guide robots in identifying and picking fruits, improving the automation level of agricultural production and reducing labor costs.

1. High-Performance Computing: The parallel processing capability of Xilinx FPGA enables machine vision algorithms to run efficiently, achieving rapid processing and analysis of image data. Compared to traditional CPU and GPU computing platforms, FPGA can significantly improve processing speed and reduce latency when processing large-scale image data, meeting the strict real-time requirements in fields such as industrial automation and intelligent security. For example, in industrial inspection, real-time detection of products on high-speed production lines can be achieved, ensuring that production efficiency is not affected.

2. Flexibility and Customizability: Users can customize the FPGA design according to the needs of different application scenarios through programming, implementing specific machine vision algorithms and functions. Whether it is the development of new algorithms or the optimization of existing algorithms, FPGA can respond quickly to meet the ever-changing business needs of enterprises. For example, in different industrial inspection scenarios, algorithms and parameters can be flexibly adjusted according to product characteristics and inspection requirements to achieve precise detection.

3. Low Power Consumption: FPGA has relatively low power consumption during operation, making it particularly suitable for scenarios with strict power requirements, such as mobile devices and embedded systems. The low power consumption characteristic not only reduces energy costs but also improves the stability and reliability of the system, extending the lifespan of the equipment. When applying machine vision systems on mobile robots in logistics warehousing, low-power FPGA can reduce battery consumption, improving the endurance of the robots.

4. Rapid Deployment: Due to the hardware reconfigurability of FPGA, users can quickly deploy developed machine vision algorithms onto FPGA, achieving rapid product launch. Compared to traditional ASIC (Application-Specific Integrated Circuit) design, the development cycle of FPGA is shorter, and costs are lower, reducing the R&D risks and costs for enterprises. Enterprises can quickly launch new products based on market demand, seizing market opportunities.

The Puzhi MPSOC development board machine vision solution provides comprehensive, efficient, and customized machine vision solutions for various application scenarios. Through careful hardware selection and complete software design, it fully leverages the advantages of the Puzhi MPSOC development board to meet the needs of different industries for machine vision. In fields such as industrial automation, intelligent security, logistics warehousing, and agriculture, this solution has broad application prospects and will strongly promote the intelligent development of various industries. With the continuous advancement of machine vision technology and the ongoing innovation of FPGA technology, the Puzhi MPSOC development board machine vision solution will also continue to optimize and improve, providing users with more powerful visual computing capabilities and higher quality application experiences.