High Dimensionality Reduction and Transfer Learning Based Ensemble Approach for Effective Car Damage Detection

Automobile damage identification is crucial for evaluating insurance claims. Conventional techniques often struggle with high-dimensional image data and diverse damage conditions. This work explores a comprehensive approach to improve the precision and effectiveness of damage detection systems. Key strategies include data cleansing and normalization for consistency, robust data augmentation to enhance generalization, and t-Distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction. EfficientNetB0 architectures are employed for ensemble modeling, using various initializations to train multiple instances independently. During inference, predictions are combined through averaging to ensure reliable and precise results. Experimental results on a car damage dataset demonstrate the proposed method's effectiveness, achieving an impressive 98% accuracy. This system significantly streamlines car damage analysis, enhancing customer satisfaction and expediting insurance claim processing.