Plastic Shampoo Bottle Dataset for Surface Defects Detection

The dataset consists of plastic shampoo bottle images annotated with five distinct categories of surface defects: Label Adhesion on Bottle (LAOB), Dent on Bottle (DOB), Scuff on Bottle (SOB), Product on Bottle (POB), and Label Tear on Bottle (LTOB). Defects are treated as individual objects within the images. A portion of the dataset includes bottles exhibiting multiple defects simultaneously, intentionally incorporated to recognize compound, real-world defect scenarios. Each defect type is additionally assigned a quality grade: marginal or unacceptable based on the measured size and severity of the defect. The images of defective bottles were manually annotated to precisely mark the spatial locations of each defect. Every defect instance was assigned a unique class label that captured both its defect type and its severity level. Specifically, adhesion_marg and adhesion_unacc were used for the marginal and unacceptable cases of LAOB; dents_marg and dents_unacc for DOB; scuff_marg and scuff_unacc for SOB; pob_marg and pob_unacc for POB; and tear_marg and tear_unacc for LTOB. Annotation was performed using rectangular or square bounding boxes that fully enclosed the visible defect regions.