Preprocessing Variations for Classification in Smart Manufacturing

Despite its contribution to Malaysia’s Gross National Income, research into automating Edible Bird’s Nest (EBN) classification is still preliminary even with its complexity as it takes into account multiple specific characteristics including colour, shape, size, and level of impurities. Furthermore, most smart manufacturing automation research is conducted under strictly controlled environments, not accounting for the possibility of low-quality visual data produced in real-life deployment settings. Thus, this paper addresses the need to automate the EBN classification process for the purpose of advancing smart manufacturing within its industry. To replicate the challenges posed by low-quality visual data commonly encountered in industrial environments, we employ a range of preprocessing techniques focused on brightness and blurriness. Subsequently, our chosen object detection algorithm, YOLOv8, was trained and evaluated on a manually collected dataset where the samples were provided by an EBN manufacturer. Variations of this dataset were created by modifying the amount of brightness and blurriness through Roboflow’s preprocessing settings. By combining both the original and preprocessed images to form the training dataset, we expose the model to the sample under desired conditions and then to conditions that lower its quality. The test results were tabulated based on the metrics: recall, precision, F1-score, mAP50 and mAP50-95. The results show that the overall performance of the deep learning model degrades when trained with preprocessed datasets compared to the dataset without preprocessing. The performance also follows the trend of increasing until a certain range for both brightness and blurriness before decreasing in performance. This trend justifies the need for research involving computer vision to investigate the optimum preprocessing configurations that would allow deep learning models to perform at their best.