# A Road Defect Detection System Using Smartphones
## 3 A Proposed Road Defect Detection System
### A Road Defect Detection with 1D-Cnn  
Recurrent Neural Networks (RNNs) are especially adept at handling temporal or sequential data, making them ideal for tasks involving continuous time series or textual data. These models possess the unique capability to remember and integrate past information with incoming data, enabling them to perform effectively in scenarios where the sequence and context of data points are critical. RNNs are particularly effective in both classification and prediction tasks. Although these tasks might appear distinct, prediction plays a crucial role in enabling RNNs to develop a nuanced internal representation of the input sequence's essential characteristics. This capability significantly enhances the RNN's training efficiency, accuracy, and generalization to new data. As a result of this sophisticated processing ability, RNNs demonstrate superior classification performance, providing users with a reliable means to assess the outcome of classification tasks [41]. Long Short-Term Memory (LSTM) networks, a subtype of Recurrent Neural Networks (RNNs), are designed to solve the problem of long-term dependencies in sequence data. Their unique architecture enables the retention of information over long periods, making them ideal for complex time series analysis. A novel approach combines LSTM with Fully Convolutional Networks (FCN) for time series sequence classification, resulting in the LSTM-FCN model. This model outperforms standard FCN models in accuracy while maintaining a small increase in size and minimal data preprocessing requirements. Further enhancing this model, the Attention LSTM Fully Convolutional Network (ALSTM-FCN) incorporates attention mechanisms to improve classification performance and allows for the visualization of LSTM decision processes, making it a sophisticated tool for time series analysis with enhanced interpretability [42]. The One-Dimensional Convolutional Neural Network (1D-CNN) proves to be a powerful tool for identifying patterns within time series data, making it particularly useful in fields like signal processing and audio analysis. In the context of time series classification, the selection of an appropriate kernel size is pivotal, as it significantly influences the model's performance. To address the challenge of determining the optimal kernel size, this paper introduces a novel architecture, the Omni-Scale 1D-CNN (OS-CNN). This architecture is designed to dynamically identify and adapt the kernel size during the training process, thereby enhancing the model's ability to extract relevant features from time series data across different scales. The OS-CNN represents a significant advancement in the utilization of 1D-CNNs for time series analysis, offering a flexible and efficient approach to capturing the complexities of temporal data [43]. In this paper, we propose road defect detection with a 1D-CNN (RDD-CNN) model. As Figure 3 presents the one-dimensional convolutional layer extracts spatial features from time-series data, while 1D Max Pooling reduces data dimensions, and Dropout prevents overfitting. The flattened layer transforms multi-dimensional feature maps into a one-dimensional vector, and the dense layer performs final classification or prediction. The Swish function is used in the one-dimensional convolutional layer, with filter sizes of 100 and 50, a kernel size of 4, and strides of 1. The final dense layer employs a SoftMax activation function, a categorical cross-entropy loss function, and an Adam optimizer. The training dataset for the RDD-CNN model uses generated data from the Automatic Data Collection System. The continuous acceleration sensor data collected while driving the vehicle is tested using our proposed RDD-CNN model. To classify three distinct road defects (i.e., speed bumps, manholes, and potholes) from the continuous data, we propose a sliding window algorithm as in Algorithm 1.