An Intelligent Hybrid Deep Learning Framework for Automated Parcel Damage Detection Using Computer Vision and CNN–SVM Classification
Authors-CH.Srinivas Reddy, Muthu Preethi
Keyword-Parcel Damage Classification, Computer Vision, Deep Learning, Convolutional Neural Net-work, Support Vector Machine, Shipment Quality Assessment, Logistics Automation, Image Classification, Damage Detection, Artificial Intelligence.
The rapid growth of e-commerce and global logistics has significantly increased the demand for reliable shipment quality inspection systems. Manual parcel inspection methods are often time-consuming, inconsistent, and unsuitable for large-scale logistics operations, leading to increased operational costs and customer dissatisfaction. This research proposes an intelligent computer vision framework that automatically detects and classifies parcel damage using a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Support Vector Ma-chines (SVM). The CNN component is employed to learn high-level visual representations from parcel images, while the SVM classifier performs accurate categorization of damage severity based on the extracted feature vectors. The proposed system identifies multiple parcel conditions, including undamaged parcels, minor damage, moderate damage, and severe damage. A compre-hensive image preprocessing pipeline involving resizing, normalization, and data augmentation improves the robustness of the learning process and enhances model generalization. The devel-oped hybrid framework is trained and evaluated using a large parcel image dataset containing multiple categories of shipment damage. Experimental evaluation demonstrates that the proposed CNN–SVM architecture achieves an overall classification accuracy of 98.8%, indicating its capability for reliable and automated parcel quality assessment. The proposed framework offers a scalable, intelligent, and practical solution for logistics companies, courier services, and e-commerce platforms by minimizing manual inspection efforts, improving shipment quality con-trol, reducing financial losses, and enhancing customer satisfaction while preserving the same implementation methodology and evaluation strategy as the original study.
Doi-[https://doi.org/10.5281/zenodo.21735266]