Automated Evaluation of Smartphone Screen Damage: A CNN-Based Image Recognition System

Chiama, Ogechi; Ganesan, Swathi

Automated Evaluation of Smartphone Screen Damage: A CNN-Based Image Recognition System

Chiama, Ogechi and Ganesan, Swathi ORCID: https://orcid.org/0000-0002-6278-2090 (2025) Automated Evaluation of Smartphone Screen Damage: A CNN-Based Image Recognition System. Artificial Intelligence and Applications, 4 (2). pp. 215-223.

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Official URL: https://doi.org/10.47852/bonviewAIA52024309

Abstract

The automation of defect detection in smartphone screens is important in ensuring fairness in the refurbished smartphone market. Conventional methods for evaluating screen damage rely on manual assessments, which are subjective, unreliable, and susceptible to human error. This paper presents a deep-learning-based approach using a convolutional neural network (CNN) to classify smartphone screens as cracked or uncracked. The CNN model was trained using a custom dataset of smartphone images, with an accuracy rate of 92.0% in classifying screen damage. CNN outperformed conventional machine learning methods in terms of feature extraction, resulting in higher precision in defect detection. However, the model encountered difficulty in identifying subtle crack patterns, fluctuations in light conditions, and overfitting resulting from the dataset’s limited diversity. Future work will focus on expanding the dataset, refining methods for data augmentation, and exploring alternative algorithms such as a transformer-based or hybrid model to improve model quality. This study aims to facilitate the standardization of defect assessment in the refurbished smartphone industry by automating screen damage detection, thereby increasing consumer confidence and boosting resale market efficiency.