Đồ án tốt nghiệp: Ứng dụng camera 3D trong việc phân loại sản phẩm theo hình dạng và kích thước

In the era of Industry 4.0, the integration of physical, digital, and biological technologies has propelled the widespread adoption of smart electronic devices across various sectors, including manufacturing. Within this context, efficient product classification and quality control are paramount. Image processing, a rapidly evolving field, plays a crucial role in these applications. This paper explores the application of 3D camera technology, which offers superior depth perception compared to human vision, for automated product sorting. The project, titled "Application of 3D Camera in Product Classification by Shape and Size," addresses the critical need for precise and rapid classification in modern industrial environments.

The target audience for this document includes students and researchers in electrical engineering, electronics and telecommunications engineering, automation, and computer vision. It is also relevant for engineers and professionals working in industrial automation, manufacturing, and quality control seeking to implement or understand vision-based product classification systems.

This academic project, "Application of 3D Camera in Product Classification by Shape and Size," presents a system designed for automated sorting based on geometric attributes. In the context of Industry 4.0, where efficient manufacturing and quality control are essential, the research leverages advanced image processing techniques to identify and classify products accurately. The core methodology involves using a 3D camera to capture visual data, followed by extensive image preprocessing. This includes noise reduction using Gaussian filters, conversion to grayscale, and advanced edge detection (Canny, Sobel gradients) and border filling to isolate distinct product shapes.
The system is implemented using the Python programming language, with the OpenCV library serving as the primary tool for image manipulation and analysis. It focuses on classifying products into basic geometric shapes—rectangles, squares, and triangles—by analyzing their unique features such as the number of vertices and the lengths of adjacent sides. Stereo camera calibration techniques are applied to accurately calculate dimensions like circumference, side lengths, and the distance of the object from the camera, thereby enabling precise size-based classification.
Experimental results demonstrate the system's ability to identify and measure products with high accuracy (around 95%) for defined shapes under ideal lighting conditions, exhibiting a fast response time. The application of this technology holds significant value for industrial automation, streamlining packaging and quality inspection processes. However, limitations include sensitivity to environmental factors like lighting and noise, challenges in recognizing multiple overlapping objects or irregularly shaped items, and current inability to classify circular objects. Future development aims to integrate the system into a full production line with a conveyor belt, enhance hardware capabilities, and refine algorithms for improved robustness, broader shape recognition, and object counting functionalities.