A hybrid deep learning CNN-ELM approach for parking space detection in Smart Cities

With each passing day, the number of smart vehicles is increasing manifold, hence, automatic/automated parking lot detection is gaining a lot of importance among Smart City applications. A robust approach is desired to identify parking spaces effectively and efficiently. This work presents a deep learning classifier based on convolutional neural network (CNN) and extreme learning machine (ELM), i.e., CNN-ELM to classify the parking space as vacant or occupied. CNN is well-known for efficient image classification, but its training time is highly influenced by backpropagation of errors in the fully connected layer. Thus, ELM is plugged into CNN to replace the fully connected layer and perform classification whereas, feature extraction is performed using CNN. The performance of CNN-ELM is validated on the publicly available PKLot dataset, which contains approximately 700,000 images categorized into sunny, overcast, and rainy weather conditions. The experimental results indicate that CNN-ELM approach outperforms other hybrid CNN models using different classifiers such as support vector machine, Xgboost, and Extra Trees in terms of sensitivity, specificity, and accuracy. The comparison of results with other state-of-the-art approaches based on accuracy and Area under the curve (AUC) score further justifies the effectiveness of the proposed approach in real-time parking space detection.