Detection of traffic density via YOLOv8 towards prediction via regularized LSTM network

This paper focuses on classifying and detecting traffic volume and congestion of a major highway and delivering predictions and forecasts of traffic congestion severity on these roadways. This would be performed by utilizing the YOLOv8 object detection model trained using the coco dataset. The dataset would be videos taken of a road overlooking passing vehicles. YOLOv8 is a deep neural network developed as a regression-based high-performance algorithm for real-time detection. ByteTrack would be utilized to have a tracking unique ID for each detected vehicle and be able to count the number of objects seen in each frame. This data would then be used to develop a Long Short-Term Memory Neural Network (LSTM) that can predict traffic congestion. While there are multiple techniques for time series predictions such as mathematical and statistical modeling, using a machine learning approach such as LSTM has advantages in robustness, accuracy, and not having to rely on simulating an environment. Afterward, regularization and merging with different optimizers on the LSTM model would be performed to improve the model's accuracy. Regularization is a technique for imposing constraints (such as L1 or L2) on the weights within LSTM nodes. This reduces overfitting and improves model performance. Multiple iterations of hyperparameter tuning would be used to determine the sets of hyperparameters and optimizers most suitable for the model that can provide accurate results.