AI-Powered Vision Inspection System for Object Classification Application - Ong, Jing Song; Hossen, J.; Em, Poh Ping; Bhuvaneswari, Thangavel; Raja, J. Emerson; Soe, Min Thu; Hidayati, Nor; Lee, Gin Chong; Alam, Sajid Abdullah (International Journal of Membrane Science and Technology, 2023)
Human operators are often susceptible to eye fatigue due to sleep deprivation and excessive workload, which may negatively impact their consistency and efficiency in performing repetitive and challenging inspection tasks. This paper presents the development of an AI-powered vision inspection system for object sorting applications, utilizing a You-Only-Look-Once (YOLO) version 3 pre-trained model based on Deep Convolutional Neural Network’s (DCNN) transfer learning technique. Feature extraction for each data point is performed using Darknet53, which subsequently trains the YOLO v3 model. The dataset is partitioned into a training set and test set at a 90:10 ratio. The trained model achieves a mean average precision (mAP) of 99.146%. Enhancing the precision and recall values of the model can be accomplished by increasing the number of dataset instances used for training.