Road Anomaly Detection Using Deep Learning Approach

Anomaly detection plays a crucial role in real-world applications by identifying instances that deviate significantly from the norm. In the context of road anomalies, deep learning models deployed in powerful processors within vehicles can effectively recognize and classify different types of road anomalies. This information can then be utilized for path planning and avoidance mechanisms in autonomous vehicles. Convolutional Neural Networksare a type of deep learning algorithm that excels at learning patterns inherent in photos and automatically distinguishing between them. Digital image processing, which involves the use of algorithms to process digital images, falls under the umbrella of signal processing. By applying deep learning techniques, digital images can be evaluated based on parameters such as Precision, Recall, and F1-Score. This comprehensive analysis of road anomaly detection using deep learning and digital image processing provides valuable insights for researchers and individuals interested in this field.