Analysis on SSD Real Time Highway Congestion Based on Convolutional Neural Network Target Detection Algorithm
Target detection is to identify and locate the object of interest from static images or video
sequences, which is one of the key tasks in the field of computer vision. Target detection
uses image processing, machine learning, deep learning and artificial intelligence
technology, which has been widely used in many fields, such as intelligent
transportation, military exercises, medical image analysis, industrial detection and so on.
However, in real-time highway congestion analysis, there are still many environmental
interference factors, such as brightness, shape, color, occlusion and so on, which makes
the research opportunities and challenges of target detection algorithm coexist. Firstly,
this paper analyzes the basic concepts of convolution neural network target detection
algorithm. Then, this paper analyzes the real-time road congestion model of SSD.
Finally, an empirical model is proposed.