The Application of Puzhi in Image Processing Scenarios

In the context of Industry 4.0 and intelligent manufacturing, the detection phase plays a key role in ensuring product quality and improving production efficiency. Image processing technology, as one of the core technologies in the detection field, can achieve rapid and accurate identification and analysis of target objects. The Puzhi development board, with its unique hardware architecture and programmable features, provides innovative solutions to the challenges of image processing in detection scenarios, meeting the stringent requirements for high precision and real-time detection across various industries.

1. Powerful Parallel Processing Capability: With abundant logic units and high-speed data pathways, it can process multiple image data channels simultaneously, significantly enhancing processing speed. For example, when processing high-definition images, it can complete complex computation tasks in a short time.

2. Highly Flexible Programmability: Supporting hardware description languages such as VHDL and Verilog, users can customize image processing algorithms according to specific detection needs, easily handling both simple edge detection and complex deep learning algorithm implementations.

3. Low Power Consumption and High Reliability: Utilizing advanced manufacturing processes, it reduces power consumption while ensuring high performance, making it suitable for long-term uninterrupted operation in detection systems, thereby reducing maintenance costs and improving system stability.

1. Target Detection and Recognition: In industrial product inspection, it is necessary to accurately identify defects such as scratches, cracks, and holes on product surfaces, as well as whether the shape and size of components meet standards. In security monitoring, it is essential to recognize target objects such as personnel and vehicles and issue alerts for abnormal behaviors.

2. Image Enhancement and Restoration: Due to factors such as the acquisition environment and lighting conditions, the captured images may have noise and blurriness. Image enhancement algorithms, such as histogram equalization and homomorphic filtering, are needed to improve the contrast and clarity of images. For image degradation caused by motion blur or defocus, image restoration algorithms should be employed for repair.

3. Real-time Performance and Accuracy: In production line inspections, products pass through detection stations at a certain speed, requiring the image processing system to complete detection and provide results in a very short time to ensure production continuity. At the same time, the detection results must be accurate and reliable to avoid false positives and missed detections, ensuring product quality.

1. Image Acquisition Module: A high-resolution, low-noise CMOS camera, such as the Sony IMX series sensor, is selected, which has high pixel count and good light sensitivity, capable of capturing clear image data. It connects to the FPGA development board via an LVDS (Low Voltage Differential Signaling) interface, ensuring high-speed and stable data transmission, while the FPGA configures the camera's operating parameters, such as exposure time, frame rate, and resolution, through an I2C interface.

2. FPGA Core Processing Unit: The Puzhi development board serves as the core of the entire system, responsible for receiving image data from the image acquisition module and performing real-time processing. Utilizing its rich logic resources and high-speed data processing capabilities, it implements various image processing algorithms and detection logic.

3. Storage Module: Configured with large-capacity high-speed DDR memory, such as DDR4 SDRAM. It is used to cache the acquired raw image data to prevent data loss; on the other hand, it provides data storage and retrieval space for algorithm execution during image processing. A multi-bank ping-pong caching mechanism is employed to improve data read and write efficiency, meeting the demands of real-time processing.

4. Communication Interface: An Ethernet interface is designed, using a Gigabit Ethernet controller chip to achieve high-speed data transmission with the host computer or other devices, transmitting processed detection results and image data to the host computer for display, storage, and further analysis. A USB interface is also configured for easy device debugging, parameter settings, and data exchange with external storage devices.

1. Algorithm Implementation:

(1) Algorithm Design Based on Hardware Description Language: Various basic image processing algorithms, such as edge detection using Sobel and Canny operators, morphological operations like erosion, dilation, opening, and closing, as well as feature extraction algorithms like Harris corner detection and SIFT feature extraction, are implemented using VHDL or Verilog hardware description languages. Hardware parallel design and pipelining techniques are employed to enhance the execution efficiency and processing speed of the algorithms.

(2) Hardware Acceleration Implementation of Deep Learning Algorithms: For complex detection tasks, such as high-precision defect detection and target recognition in complex scenes, deep learning algorithms are introduced. Using Xilinx development tools, deep learning models (such as Convolutional Neural Networks, CNN) are quantized, compiled, and mapped to FPGA hardware resources, achieving hardware acceleration of deep learning algorithms while ensuring detection accuracy and meeting real-time requirements.

2. Driver Development: Develop driver programs for the CMOS camera to control data transmission and parameter configuration between the camera and FPGA; develop driver programs for communication interfaces such as Ethernet and USB to ensure accurate and stable data transmission between the FPGA and external devices.

3. Host Computer Software: Using development languages such as C# and Qt, user-friendly host computer software is developed. It implements the setting of detection parameters, such as detection thresholds and image preprocessing parameters; displays detection results in real-time, including the positions, types, and quantities of detected target objects; and provides data storage and report generation functions for subsequent analysis and traceability of detection data.

1. Image Acquisition: The CMOS camera captures image data according to the set parameters and transmits the data to the FPGA development board via the LVDS interface.

2. Image Preprocessing: The acquired raw images undergo denoising, using algorithms such as median filtering and Gaussian filtering to remove salt-and-pepper noise and Gaussian noise; grayscale processing is performed to convert color images into grayscale images for subsequent processing; methods such as histogram equalization are used to enhance the contrast of the images, improving their visual effects.

3. Feature Extraction: Based on the requirements of the detection task, appropriate feature extraction algorithms are selected. For shape detection, the contours and geometric shape features of target objects can be extracted; for defect detection, features such as edges and textures of defects are extracted. For example, in circuit board defect detection, edge detection algorithms are used to extract the edge features of the circuit board's lines and compare them with standard templates to determine if there are defects such as open circuits or short circuits.

4. Target Detection and Analysis: The extracted features are matched and analyzed against preset standard templates or models. If it is a deep learning-based detection model, the preprocessed images are input into the model, and through the model's inference calculations, detection results are output, determining whether target objects or defects exist in the images and identifying their positions, sizes, and types.

5. Result Output: The detection results are transmitted to the host computer for display and storage via the Ethernet interface; simultaneously, based on the detection results, control signal output interfaces, such as GPIO interfaces, are used to control external devices, such as alarm devices and sorting devices, to achieve real-time processing of the detected objects.

1. Excellent Real-time Performance: The parallel processing characteristics and hardware acceleration capabilities of FPGA enable the image processing and detection processes to be completed in a very short time, meeting the demands of high-speed production lines and real-time monitoring scenarios.

2. High Customization: Users can flexibly adjust hardware configurations and software algorithms according to different detection tasks and application scenarios, achieving personalized detection solutions.

3. High Cost-effectiveness: Compared to dedicated image processing chips and complex deep learning computing platforms, the Puzhi development board offers a high cost-performance ratio, meeting high-performance detection needs while effectively controlling costs.

1. Surface Defect Detection of Automotive Parts: In automotive parts production lines, this solution is used to detect surface defects of automotive wheels, engine blocks, and other components. By real-time capturing surface images of components and processing them through FPGA, it can quickly and accurately detect defects such as scratches, sand holes, and air holes, achieving a detection accuracy rate of over 98%, significantly improving product quality and production efficiency.

2. Quality Inspection of Agricultural Products: In the agricultural product processing industry, the external quality of fruits and vegetables is inspected. By recognizing features such as the size, shape, color, and surface blemishes of fruits, it achieves grading and sorting of fruits, enhancing the marketability and competitiveness of agricultural products.