A Self-Adaptive Chimp-Driven Modified Deep Learning Framework for Autonomous Vehicles to Obtain Autonomous Object Classification

The development of the future's smart cities will increasingly depend on autonomous vehicles. For example, object detection, tracking, path planning, and sentiment or intent recognition, among other things, have all been the subject of several suggestions in recent years in an effort to address...

Full description

Saved in:
Bibliographic Details
Published in:Electric power components and systems Vol. 51; no. 17; pp. 1895 - 1909
Main Author: Rajasekaran, Suresh Babu
Format: Journal Article
Language:English
Published: Philadelphia Taylor & Francis 21.10.2023
Taylor & Francis Ltd
Subjects:
Algorithms
and image augmentation
Autonomous vehicles
Deep learning
Error analysis
Geometric transformation
geometric transformations
histogram equalization-based contrast enhancement
Image contrast
Image enhancement
Image quality
Machine learning
object detection
Object recognition
Optimization
Performance evaluation
Performance measurement
Recurrent neural networks
region of interest
Self adaptive control systems
ISSN:1532-5008, 1532-5016
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The development of the future's smart cities will increasingly depend on autonomous vehicles. For example, object detection, tracking, path planning, and sentiment or intent recognition, among other things, have all been the subject of several suggestions in recent years in an effort to address specific components of the working pipeline and develop a useful end-to-end system. This article presents a simple benchmark to evaluate the performance of object identification algorithms under deteriorating image quality. In this article, the input image is taken from the car object detection dataset, then, it gets preprocessed. The preprocessing involved Histogram equalization-based contrast enhancement, geometric transformations, and image augmentation. After getting preprocessed the region of interest is segmented with the help of a mask convolutional recurrent neural network (MCRNN). Then, the segmented region is classified using the long short-term memory (LSTM) with the self-improved chimp optimization algorithm. Object detection is performed through the proposed model called MCRNN-SICLSTM. The implementation is performed using MATLAB software. The performance of the proposed model is compared with the existing techniques using the performance metrics accuracy, precision, recall, F-Measure, mean absolute error, mean square error, false positive rate, false negative rate. The accuracy achieved is 99.23%.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1532-5008
1532-5016
DOI:10.1080/15325008.2023.2203713