Streamlining the Process of E-Commerce: Automated Damage Detection Using MobileNetV2

Abstract

Online stores are also confronting greater challenges in effectively controlling product quality, especially in processing damaged products during order shipment and return. Image classification techniques based on images have become a promising alternative to mechanize qualityinspection and improve efficiency. In this work, weintroduce a deep learning model based on the MobileNetV2 structure for image classification of products as damaged or not damaged. The model istrainedonaproprietary datasetoflabeledimages from actual e-commerce environments. Images are preprocessed with resizing and normalization to ensure consistency and compatibility with convolutional neural networks for enhanced model performance. The compact yet efficient MobileNetV2 model is fine-tuned to maximize classificationaccuracywhileensuringcomputationalefficiency, making it ideal for deployment in resource-limited environments like warehouses or mobile devices. Experimental results confirm that the proposed approach achieves very high accuracy on both training and validation sets, thus indicating excellent generalization capability. Performance is assessed based on crucial metrics such as accuracy, precision, recall,and F1-score. Our results indicate that the incorporation of deep learning for damage detection can prove to be very useful in improving the operational efficiency of e-commerce websites by lessening the overhead of manual inspection and lowering error rates. The framework outlined is an economical and scalable solution for current e-commerce quality control and logistics systems.

Introduction

I. Introduction

• The rise of e-commerce platforms (Amazon, eBay, Wish) has improved consumer convenience but complicated returns management.

• Returns often result from damage during shipping, leading to customer dissatisfaction and manual inspection bottlenecks.

• Traditional return processes involve human staff reviewing images or items, which is slow, error-prone, and inefficient.

• The proposed solution: an automated visual inspection system using MobileNetV2, a lightweight deep learning model, to classify returned product images as damaged or undamaged.

II. Literature Review & Research Gaps

Key related works and gaps identified:

Identified Need: A lightweight, real-world deployable image classification model tailored for return verification in e-commerce.

III. Methodology

A. Dataset Preparation

• Custom dataset with two categories: Damaged and Undamaged products.

• Images resized to 224×224, normalized using PyTorch, and loaded in batches of 32.

• Applied data shuffling to improve generalization.

B. Model Design

• Used MobileNetV2, pre-trained on ImageNet.

• Final classification layer replaced to output two classes (damaged/undamaged).

• Training Details:

  • Optimizer: Adam (LR = 0.001)

  • Loss Function: CrossEntropyLoss

  • Training over 5 epochs

  • Device: GPU or CPU depending on availability

C. Model Evaluation

• Evaluated on 50 validation images from the "damaged" class.

• Model set to eval() mode to ensure stable prediction behavior.

• Achieved 92% accuracy in damage detection.

IV. Results and Discussion

A. Performance Summary

• MobileNetV2 showed high classification accuracy (92%) with efficient resource usage.

• Ideal for real-time, edge-based deployment in e-commerce return systems.

B. Comparison with Other Models

C. Strengths & Limitations

• Strengths:

  • Fast and lightweight

  • Suitable for real-time use

  • Reduces human error in return processing

• Limitations:

  • Struggles with complex lighting/backgrounds

  • Can't detect degrees of damage (just binary)

  • Real-world diversity still underrepresented in training data

Conclusion

This work illustrates the utility of using deep learning methods, namely the MobileNetV2 network, to streamline e-commerce processes by automatically classifying broken and intact products. Through training the network with a labeled dataset of product images, we have illustrated that light-weight convolutional networks canoffer high classification performancewith low computational cost. This proves to be particularly important for application in real-world settings where computational resources are limited, for instance in fulfillment centers or mobile examination stations. Experiments performed using stratified training and validation techniques illustrate that the suggested method minimizes the level of manual intervention needed inproduct inspection and return authentication processes. Performance of the model was assessed with respect to principal metrics such as accuracy, precision, recall, and F1- score, which all prove that the system is consistent in classifyingproductsasdamaged orundamaged.Thefindings corroborate the premise that theinclusionofdeep learningin e-commerce processes can facilitate dramatic process improvements, especially quality control and return fraud detection. The conclusion can be drawn that the use of MobileNetV2 as a classifier in e-commerce not only increases the process speed of the product verification but also provides better consistency and reliability than manual checks. In subsequent work, we hope to generalize this method bycombining real-time data augmentation and edge- based deployment methods to further minimize latency and enhance adaptability. The approach can also be generalizedto identify particular forms of damage or product types, thereby enhancing its usability across different industries within the e-commerce ecosystem.

References

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Copyright

Copyright © 2025 Mrs. Aparna Shrivastava, Dhruv Trivedi, Harsh Mishra, Ashu Sharma, Abhishek Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.