An Image Feature Extraction Method for Quick Inspection and Fault Detection of Objects in Production Systems

In modern industry, continuous production systems require the integration of monitoring systems capable of real-time inspection and anomaly detection of final products. This necessitates high-speed capture of product images and rapid information processing to determine the rejection of defective products. To address the challenges of reducing processing time and increasing fault recognition accuracy in products, a novel detection method based on image analysis and subsequent classification is proposed. While the techniques employed, such as image histograms and Principal Component Analysis, are well-established in image and data processing, the innovative integration of these methods in this approach provides a streamlined and highly efficient solution for classification. Specifically, the classification process relies on prior image processing, where the histograms of the 3 color channels of each image are obtained and concatenated, then PCA is applied, resulting in separable clusters. Cluster classification is achieved through a simple SVM. A significant advantage of this method is that it requires a reduced amount of image data for training the SVM, simplifying this stage of the process. The proposed method is benchmarked using a dataset of images aimed at detecting defects in a pill blister pack, which may include missing pills, while a data augmentation process is implemented. The relationship between the image histogram and the presence of faults is demonstrated under controlled lighting and sensor arrangement environments.