Multi-aspect detection and classification with multi-feed dynamic frame skipping in vehicle of internet things

Abstract

Consumer demand for automobiles is changing because of the vehicle’s dependability and utility, and the superb design and high comfort make the vehicle a wealthy object class. The creation of object classes necessitates the creation of more sophisticated computer vision models. However, the critical issue is image quality, determined by lighting conditions, viewing angle, and physical vehicle construction. This work focuses on creating and implementing a deep learning-based traffic analysis system. Using a variety of video feeds and vehicle information, the developed model recognizes, categorizes, and counts vehicles in real-time traffic flow. The dynamic skipping method offered in the developed model speeds up the processing of a lengthy video stream while ensuring that the video picture is delivered accurately to the viewer. In real-time traffic, standard vehicle retrieval may assist in determining the make, model, and year of the vehicle. Previous MobileNet and VGG19 models achieved F-values of 0.81 and 0.91, respectively. However, the proposed solution raises MobileNet’s frame rate from 71.2 to 89.17 and VGG19’s frame rate from 48.2 to 59.14. The method may be applied to a wide range of applications that require a dedicated zone to monitor real-time data analysis and normal multimedia operations.

Smart Optimizer Selection Technique: A Comparative Study of Modified Densnet201 With Other Deep Learning Models

“…A modified version of MobileNet based on the increase in frame rate is proposed by Ahmed et al to achieve higher F-values. It could be applied to diverse applications supporting real-time traffic data analysis [2]. Another deep learning model based on ResNet-50 is presented for vehicle localization and classification using real data from traffic surveillance cameras [12].…”