Thiết kế hệ thống phát hiện và nhận dạng khuyết tật trên gỗ thanh

The manual inspection of wood quality in the timber industry is prone to human error, tedium, and inconsistency, often relying on the subjective judgment of trained operators. This traditional approach significantly hinders efficiency and quality assurance in a sector where material integrity, particularly for components like support beams, is paramount. Defects such as knots, cracks, and wane can compromise the structural stability and strength of wood, leading to material waste if not accurately identified and precisely located. Addressing these challenges, this study proposes an automated system for detecting and identifying defects on wood bars using advanced computer vision techniques. By leveraging Python/OpenCV, the research aims to replace manual processes with a rapid, objective, and more reliable method for quality control in wood processing.

Engineers and researchers in computer vision, automation, and material quality control, particularly those in the timber processing and wood manufacturing industries.

This research introduces a robust automated system for the detection and identification of defects on wood bars, designed to overcome the limitations of manual inspection methods prevalent in the timber industry. The proposed system integrates several key computer vision stages, primarily implemented using Python/OpenCV. Initially, it performs wood shape recognition by converting input images to grayscale, applying smoothing filters, and then utilizing thresholding and contour detection to accurately delineate the wood object. A crucial aspect of this system is its capability for precise measurement, achieved by establishing a pixel-to-millimeter conversion ratio based on a known reference object (e.g., a coin) within the image. For identifying specific defects on wood bars, the system employs a vector quantization method inspired by neural network principles. This involves training the system with a dataset of approximately 3000 images containing various defect types, enabling it to classify new inputs by comparing them to the trained data. The experimental results demonstrate a high accuracy rate, ranging from 94% to 99%, for defect identification. The paper concludes by highlighting the effectiveness of the developed algorithms for both identifying and classifying defects, and suggests future enhancements to integrate the system with industrial hardware such as conveyor belts and automated mechanisms for defect removal, thereby paving the way for a fully automated quality control pipeline in wood processing.