Abstract—Computer vision techniques have been widely used in industrial manufacturing for automation, monitoring, quality assessment and inspection. Specifically, some techniques are being deployed to detect and identify products’ defects instead of human operators. In this paper, a vision system is proposed to inspect and assess the quality of applying plastic lids on wet wipes packs at a manufacturing company in Jordan. Currently, the manufacturing company uses automatic lid applicator that has no visual control system and thus, a human operator is needed to inspect every pack. This process includes detecting the position and orientation of each lid to assure that it has been applied correctly at the center of the pack above specific label. Automating this inspection process would save time and efforts and increase the productivity. The proposed inspection system has the following modules: (1) Pack and lid detection and segmentation using YOLOv8s-Seg algorithm; (2) distance to border (DtB) extraction between the pack centroid and lid boundary; and (3) inspection module using linear Support Vector Machine (SVM). A segmentation dataset of 319 different images of wet wipes packs was constructed using the Segment Anything Model (SAM). The proposed inspection has been tested on different wet wipes packs. Experimental results successfully demonstrated the efficiency of the inspection system.