In the competitive landscape of steel-strip production, ensuring the high quality of steel surfaces is paramount. Traditionally, human visual inspection has been the primary method for detecting defects, but it suffers from limitations such as reliability, cost, processing time, and accuracy. Visual inspection technologies, particularly automation techniques, have been introduced to address these shortcomings. This paper conducts a thorough survey examining vision-based methodologies related to detecting and classifying surface defects on steel products. These methodologies encompass statistical, spectral, texture segmentation based methods, and machine learning-driven approaches. Furthermore, various classification algorithms, categorized into supervised, semi-supervised, and unsupervised techniques, are discussed. Additionally, the paper outlines the future direction of research focus.

Accurate assessment of water quality is crucial for protecting public health and promoting environmental sustainability. Conventional laboratory-based methods for evaluating microbial contaminants are often time-consuming, resource-intensive, and reactive in nature, limiting their effectiveness for real-time water quality monitoring and management. This study examines the application of data-driven machine learning models to predict E. coli concentrations in Midmar Dam, utilizing readily available physicochemical parameters. A comparative analysis was conducted using five classical standalone ML algorithms: Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost). These models were assessed based on their predictive performance using standard error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Among the models evaluated, the kNN algorithm demonstrated superior performance, achieving the lowest MSE and RMSE values, thereby highlighting its effectiveness in capturing the complex relationships between physicochemical indicators and microbial contamination levels. The findings demonstrate the potential of ML-based approaches to serve as efficient, scalable, and proactive tools for sustainable water-quality monitoring and management in dams.

Hot-rolled steel production is a critical process in the manufacturing industry, with surface defects posing significant challenges for quality control. Accurate classification of these defects ensures product quality and prevents costly rejections. Many studies have focused on employing CNN-based methods to categorize steel surface defects. However, individual CNN models exhibit inherent differences across various aspects, encompassing distinct architectures, differing levels of bias, and variances. On the other hand, individual models may fall short of delivering desirable results when applied to specific datasets. Acknowledging the robust performance shown by ensemble models, coupled with their unique ability to reconcile model bias and variance, this study proposes a new approach using transfer learning techniques to introduce an innovative ensemble model for accurately classifying surface defects in hot-rolled steel. Our proposed ensemble model combines three distinct pre-trained CNN architectures. Each model is individually trained on subsets of defect images to capture diverse features in various defect types effectively. Our results of extensive experimentation on benchmark NEU and X-steel surface defect (X-SDD) datasets indicate that the presented ensemble model outperforms existing methods, achieving classification accuracy of 100% on the NEU dataset and 99.27% on the X-SDD dataset.