Customized CNN-Based Condition Monitoring of Road Traffic for Intelligent Transportation Routing

This study wants to make road traffic monitoring better by using a complete way that includes many necessary tasks. The study begins by carefully fixing the images to improve the quality of the seen information. Sophisticated deep learning methods are then used to spot helmets using computer vision. This makes road safety better by finding people wearing helmets. The study also investigates getting car license plates. It uses a mix of image processing and advanced ways to read letters and numbers in images to get correct identification. Counting cars, a key part of studying traffic movement is done using advanced computer vision techniques. These methods use hard algorithms to dependably find and count cars in images. The research not only numbers but also uses machine learning to sort cars into different types. This helps us understand more about the make-up of traffic. Hidden Markov Models (HMMs) are used in time study to get features from video data. This helps us find and study patterns in a row, and changes. This makes understanding how a car moves over time better. To make road traffic sorting better, a new type of Custom Hierarchical Convolutional Neural Network (HCNN) is suggested. This HCNN uses the good parts of 1D and 2D CNNs. It can get important features from both image and video data in a big way. The use of HCNN helps make the identification of vehicle types better, which leads to smarter study of road traffic situations. This study's findings are very important for smart transport systems, city planning and traffic control.