In recent decades, data collection technologies have evolved to facilitate the monitoring and improvement of numerous activities and processes in everyday human life. Their evolution is propelled by the advancement of artificial intelligence (AI), which aims to emulate human intelligence in the execution of related tasks. The remarkable success of deep learning (DL) and computer vision (CV) on image data prompted researchers to consider its application to time series and multivariate data. In this context, time series imaging has been identified as the research field for the transformation of time series data (a one-dimensional data format) into images (a two-dimensional data format). These data can be the variables or features of a system or phenomenon under consideration. State-of-the-art techniques for time series imaging include recurrence plot (RP), Gramian angular field (GAF), and Markov transition field (MTF). This paper proposes a novel, robust, and simple technique of time series imaging using Grayscale Fingerprint Features Field Imaging (G3FI). This novel technique is distinguished by the low resolution of the resulting image and the simplicity of the transformation procedure. The efficacy of the novel and state-of-the-art techniques for enhancing the performance of CNN-based classification models on time series datasets is thoroughly examined and compared.

This paper presents a comparative study of binarization techniques for automated defect detection in dye penetrant testing (DPT) images. We evaluate established methods, including global, adaptive, and histogram-based thresholding, against three novel machine learning-assisted approaches, Soft Binarization (SoBin), Delta Binarization (DeBin), and Convolutional Autoencoder Binarization (AutoBin), using a real-world dataset from an automated DPT system inspecting stainless steel pipes. Performance is assessed with both pixel-level and region-level metrics, with particular emphasis on the influence of defect saturation. Defect saturation is quantified as the mean saturation value of all pixels belonging to a given defect, and defects are grouped into ten categories spanning from low (60–68) to high (132–140) mean saturation. Our results demonstrate that for lower mean defect saturation values, methods such as AutoBin_Triangle, HSV_global_70, and SoBin achieve superior Intersection over Union (IoU) and high true positive rates. In contrast, methods based primarily on global thresholding of the saturation channel tend to perform competitively on images with higher defect saturation levels, reflecting their sensitivity to stronger color signals. Moreover, depending on the method, nearly perfect region-level true positive rates (${\mathrm{TPR}}_{\mathrm{region}}$) or minimal false positive rates (${\mathrm{FPR}}_{\mathrm{region}}$) can be attained, emphasizing the trade-off that different models offer distinct strengths and weaknesses, which necessitates selecting the optimal method based on the specific quality control requirements and risk tolerances of the industrial process. These findings underscore the critical importance of defect saturation as a cue for both human and computer vision systems and provide valuable insights for developing robust automated quality control and predictive quality algorithms.

In recent decades, the data collection technologies have evolved to facilitate the monitoring and improvement of numerous activities and processes in everyday human life. The evolution is propelled by the advancement of artificial intelligence (AI), which aims to emulate human intelligence in the execution of related tasks. The remarkable success of deep learning (DL) and computer vision (CV) on image data prompted researchers to consider its application to time series and multivariate data. In this context, time-series imaging has been identified as the research field for the transformation of time-series data (one-dimensional data format) into images (two-dimensional data format). State-of-art techniques of time-series imaging are Recurrence Plot (RP), Gramian Angular Field (GAF), and Markov Transition Field (MTF). This paper proposes a novel, robust and simple technique of time-series imaging using Grayscale Fingerprint Features Field Imaging (G3FI). The novel technique is distinguished by its low resolution of the resulting image and the simplicity of the transformation procedure. The efficacy of the novel and state-of-the-art techniques for enhancing the performance of CNN-based classification models on time-series datasets is thoroughly examined and compared.