Real-Time Vehicle Classification Using LSTM Optimized by Oppositional-Based Wild Horse Optimization

Classifying vehicles in real time was necessary to manage and plan road traffic and avoid frequent traffic jams, traffic violations, and fatal traffic accidents.However, detecting vehicles at night presents a significant challenge, requiring the classification algorithm to be tested under diverse conditions, such as rainy weather, cloudy weather, low illumination, and others, which makes identifying vehicles a complicated task.This paper detected and classifiess vehicle through YOLO-v2, ResNet50, and an optimally configured Long Short-Term Memory (LSTM).But figuring out the best hyperparameters by trial and error took longer and was more complicated.The research resolved the computational time and complexity by involving Oppositional-based Wild Horse Optimization (OWHO) techniques to identify the optimal hyperparameters for LSTM.The result showed that the proposed technique was better, with an average accuracy of 97.38% in classifying vehicles, which was better than other techniques.