Performance and Interpretability Analysis of a 3-Block CNN for Vehicle Detection(78.54% accuracy)
Performance and Interpretability Analysis of a 3-Block CNN for Vehicle Detection(78.54% accuracy)
Safety in autonomous vehicle (AV) perception requires models that are not only accurate but also interpretable and robust to environmental noise. This technical note details the development of a custom 3-block Convolutional Neural Network (CNN) trained from scratch on a dataset of 26,378 vehicle images. We demonstrate a test accuracy of 78.54% with a negligible generalization gap (0.06%). Crucially, we utilize Gradient-weighted Class Activation Mapping (Grad-CAM) to prove that the model's decision-making is grounded in structural vehicle geometry rather than spurious background correlations.
model, research, classification, accuracy, analysis, detection, technical, Vehicle, english, CNN, note
model, research, classification, accuracy, analysis, detection, technical, Vehicle, english, CNN, note
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