SE-Enhanced Ensembled Deep Learning Framework for Parking Space Classification

A parking space system is a vital component of a smart transport management system, and it helps in managing parking spaces, reducing traffic congestion, and improving mobility. Deep learning helps in releasing smart parking systems. Despite their higher computational efficiency, lightweight convolutional neural networks (CNNs) may have limited feature representation capabilities and suffer from the vanishing gradient problem, which can reduce classification performance under challenging parking scenarios. To address this limitation, lightweight CNN models, such as MobileNetV2, EfficientNetB0, DenseNet, and ConvNeXt, are considered, with squeeze-and-excitation (SE) blocks incorporated to enhance channel-wise feature recalibration and stabilize gradients throughout the network. MobileNetV2 captures discriminative local features. EfficientNetB0 learns multi-scale semantic representations through compound scaling. DenseNet promotes hierarchical feature reuse. ConvNeXt extracts robust contextual features. The extracted deep features are subsequently reduced in dimensionality using Uniform Manifold Approximation and Projection (UMAP) while preserving the underlying manifold structure and removing redundant information. The reduced features are then classified by the ensembling of XGBoost, LightGBM, and support vector machine (SVM) classifiers, and their predictions are combined through a weighted stacking ensemble to exploit their complementary strengths and improve generalization. The proposed framework was experimentally evaluated on the PKLot, CNRPark and CNRPark + Ext datasets, achieving classification accuracies of 99.3%, 98.7% and 99.1%, respectively, demonstrating its effectiveness and robustness for real-world smart-city parking applications.