ENHANCING REVERSE LOGISTICS EFFICIENCY THROUGH AI-SUPPORTED PARCEL DAMAGE CLASSIFICATION
International Journal of Engineering and Innovative Research, vol.7, no.2, 2025 (Peer-Reviewed Journal)
- Publication Type: Article / Article
- Volume: 7 Issue: 2
- Publication Date: 2025
- Doi Number: 10.47933/ijeir.1772209
- Journal Name: International Journal of Engineering and Innovative Research
- Isparta University of Applied Sciences Affiliated: Yes
Abstract
This study introduces an automated analysis method that uses AI and image processing to check the physical condition of boxes, aiming to support reverse logistics in cargo transport. The system processes images of cardboard boxes moving along a conveyor belt, using techniques like background removal, masking, and morphological operations to calculate damage scores. Based on these scores, it can accurately sort boxes into three categories: “Intact,” “Slightly Damaged,” and “Severely Damaged.” The low variance in the results shows the model is stable and consistent in its assessments. Compared to manual checks, this approach is faster, more reliable, and more structured—helping lower reverse logistics costs and improve customer satisfaction. Overall, the study shows how AI-driven image analysis can boost both efficiency and service quality in the logistics industry.