# Exploring the ViDiDetect Tool for Automated Defect Detection in Manufacturing with Machine Vision
## 2 State of the Art  
A vision system is a technology that combines the capabilities of cameras, software, and algorithms to analyze images and inspect products and production processes. The industrial vision system processes the physical characteristics of the tested objects, which allows it to analyze their geometry, location, or surface condition. The most popular tasks performed by the systems include the following [8; 9; 10; 11]:  
* Reading barcodes;
* OCR, OCV, i.e., recognizing or verifying text;
* Quality control;
* Measurement of elements;
* Recognizing the shape, presence, and location of objects;
* Identification by comparing features with a model;
* Surface control (roughness, scratches, defects). The field of machine vision for cutting tool wear assessment has witnessed significant advancements in recent years. Researchers and manufacturers have developed innovative techniques and systems that have improved the accuracy, speed, and reliability of wear detection and quantification. In this section, we explore some of the state-of-the-art approaches and technologies in the application of machine vision systems for cutting tool wear assessment [12; 13]. High-resolution imaging: One key aspect of accurate tool wear assessment is the ability to capture detailed and high-resolution images of the cutting tools. Advancements in imaging technology, such as high-definition cameras and microscopy systems, have enabled the acquisition of clear and precise images, even at micro-scale levels. These high-resolution images provide a wealth of information for wear analysis and facilitate the detection and quantification of subtle wear features [14; 15; 16]. Image processing algorithms: Machine vision systems employ sophisticated image processing algorithms to analyze tool images and extract relevant wear information. Various techniques, such as edge detection, thresholding, feature extraction, and pattern recognition, are utilized to identify wear patterns and quantify wear parameters. Advanced machine learning algorithms, including convolutional neural networks (CNNs), have also been applied to improve the accuracy and automation of wear assessment [17; 18; 19]. Multispectral and hyperspectral imaging: Traditional grayscale or color imaging may not always capture certain wear characteristics effectively. To overcome this limitation, multispectral and hyperspectral imaging techniques have been explored for cutting tool wear assessment. These techniques involve capturing images across a wide range of wavelengths, allowing for enhanced differentiation of wear features, such as variations in surface texture or color changes due to wear [20; 21]. Real-time monitoring: Real-time monitoring of cutting tool wear is crucial for proactive maintenance and avoiding unexpected tool failures. Machine vision systems have been integrated with real-time data acquisition and analysis capabilities, enabling continuous wear assessment during the machining process. By leveraging high-speed image processing techniques and parallel computing, these systems can provide instantaneous feedback on tool condition, allowing operators to make timely decisions and schedule tool changes optimally [22; 23]. Integration with manufacturing systems: Machine vision systems for tool wear assessment are increasingly being integrated with other manufacturing systems, such as computer-aided manufacturing (CAM) and computerized numerical control (CNC) machines. This integration enables seamless data exchange, facilitating process optimization and adaptive machining strategies. By integrating machine vision with manufacturingsystems, manufacturers can achieve a closed-loop feedback mechanism that optimizes tool life and machining efficiency [24; 25]. Automated tool life prediction: Predicting the remaining useful life of cutting tools is a valuable capability for production planning and scheduling. Advanced machine vision systems, combined with predictive analytics and machine learning algorithms, have made significant progress in automated tool life prediction.