Method for Image-Based Preliminary Assessment of Car Park for the Disabled and the Elderly Using Convolutional Neural Networks and Transfer Learning.

It is critical to assess the standards of disabled facilities in order to ensure the comfort and safety of disabled individuals who use them. In this study, deep convolutional neural networks (CNNs) with multi-label classification capability are employed for a preliminary evaluation of the car park for the disabled and the elderly in accordance with ministerial regulations, reducing the burden of on-site inspection by specialists. Using a transfer learning technique, the weights of an Inception-V3, Xception, and EfficientNet-B2 architectures previously trained on the ImageNet dataset were updated with the disabled car park image dataset. We used 4,812 training images and 355 test images to train, evaluate, and compare the model. The results revealed that the EfficientNet-B2 model yielded the best performance for 5 out of 6 classes, with the F1-score between 79.8% and 95.6%. In contrast, the remaining one class was best predicted by the Xception model, where the F1-score was 83.33%. This implies that it is possible to apply CNNs to aid in the evaluation of handicap facilities.