Vehicle image damage detection based on Convolutional Neural Networks
This study aims to explore the application of Convolutional Neural Networks(CNN)in the identification of damages in car images.By collecting a dataset of car images with varying degrees of damage,a CNN model was constructed and subjected to an in-depth evaluation using key performance indicators such as accuracy.The experiment compared the effects of various loss functions on model performance,and the analysis revealed that the CNN model employing Sparse Categorical Cross-Entropy loss function exhibited superior performance,achieving an accuracy rate of 97%.This finding demonstrates the significant advantage of Sparse Categorical Cross-Entropy in enhancing model accuracy.Overall,this research provides strong support for the use of CNN in the identification of damages in car images.
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