Vision-Based Data-Driven Modeling Vehicle Detection in Videos Using Convolutional Neural Network

Abstract

Object detection is a vital component for autonomous driving, and autonomous cars rely on perception of their surroundings to ensure safe and robust driving performance. It shows how the perception system makes use of object identification algorithms to precisely identify nearby items like pedestrians, cars, traffic signs, and barriers. It goes on to say that detecting and localising these things in real-time depends greatly on deep learning-based object detectors. The most recent object detectors and unresolved issues with their integration into autonomous vehicles are also covered in the essay. It mentions that deep learning visual classification methods have achieved enormous accuracy in classifying visual scenes; it makes use of the convolutional neural network. However, it points out that the visual classifiers face difficulties examining the scenes in dark visible areas, especially during the nighttime, and in identifying the contexts of the scenes.