Vehicle detection systems for intelligent driving using deep convolutional neural networks (original) (raw)
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Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a challenging problem that must be solved. Hardware-based solutions such as radars and LIDAR are been proposed but are too expensive to be maintained and produce little valuable information to human operators at traffic monitoring systems. Software based solutions using traditional algorithms such as Histogram of Gradients (HOG) and Gaussian Mixed Model (GMM) are computationally slow and not suitable for real-time traffic detection. erefore, the paper will review and evaluate different vehicle detection methods. In addition, a method of utilizing Convolutional Neural Network (CNN) is used for the detection of vehicles from roadway camera outputs to apply video processing techniques and extract the desired information. Specifically, the paper utilized the YOLOv5s architecture coupled with k-means algorithm to perform anchor box optimization under different illumination levels. Results from the simulated and evaluated algorithm showed that the proposed model was able to achieve a mAP of 97.8 in the daytime dataset and 95.1 in the nighttime dataset.
A Model Based on Convolutional Neural Network (CNN) for Vehicle Classification
IEEE, 2021
The Convolutional Neural Network (CNN) is a form of artificial neural network that has become very popular in computer vision. We proposed a convolutional neural network for classifying common types of vehicles in our country in this paper. Vehicle classification is essential in many applications, including surveillance protection systems and traffic control systems. We raised these concerns and set a goal to find a way to eliminate traffic-related road accidents. The most challenging aspect of computer vision is achieving effective outcomes in order to execute a device due to variations of data shapes and colors. We used three learning methods to identify the vehicle: MobileNetV2, DenseNet, and VGG 19, and demonstrated the methods detection accuracy. Convolutional neural networks are capable of performing all three approaches with grace. The system performs impressively on a real-time standard dataset-the Nepal dataset, which contains 4800 photographs of vehicles. DenseNet has a training accuracy of 94.32 % and a validation accuracy of 95.37%. Furthermore, the VGG 19 has a training accuracy of 91.94 % and a validation accuracy of 92.68 %. The MobileNetV2 architecture has the best accuracy, with a training accuracy of 97.01% and validation accuracy of 98.10%.
An efficient object detection by autonomous vehicle using deep learning
International Journal of Electrical and Computer Engineering (IJECE), 2024
The automation industries have been developing since the first demonstration in the period 1980 to 2000 it is mainly used on automated driving vehicle. Now a day's automotive companies, technology companies, government bodies, research institutions and academia, investors and venture capitalists are interested in autonomous vehicles. In this work, object detection on road is proposed, which uses deep learning (DL) algorithms. You only look once (YOLO V3, V4, V5). In this system object detection on the road data set is taken as input and the objects are mainly on-road vehicles, traffic signals, cars, trucks and buses. These inputs are given to the models to predict and detect the objects. The Performance of the proposed system is compared with performance of deep learning algorithms convolution neural network (CNN). The proposed system accuracy greater than 76.5% to 93.3%, mean average precision (Map) and frame per second (FPS) are 0.895 and 43.95%.
A Vehicle Detection Approach using Deep Learning Methodologies
ArXiv, 2018
The purpose of this study is to successfully train our vehicle detector using R-CNN, Faster R-CNN deep learning methods on a sample vehicle data sets and to optimize the success rate of the trained detector by providing efficient results for vehicle detection by testing the trained vehicle detector on the test data. The working method consists of six main stages. These are respectively; loading the data set, the design of the convolutional neural network, configuration of training options, training of the Faster R-CNN object detector and evaluation of trained detector. In addition, in the scope of the study, Faster R-CNN, R-CNN deep learning methods were mentioned and experimental analysis comparisons were made with the results obtained from vehicle detection.
Image Recognition by Using a Convolutional Neural Network to Identify Objects for Driverless Car
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The concept of the paper was inspired by the recent surge in the automated car industry. The designed car was capable of detecting the road signals and taking the right and left turns accordingly. Object detection is a key ability required by most computer used in automated vehicles. The latest research in this area has been making great progress in many directions. Object detection and tracking has a variety of uses, our paper explain how to use convolutional neural network for object detection in autonomous vehicles. Automatic car always has the potential to solve traffic problems with the help of Convolution Neural Network (CNN). However, in the current scenario complete autonomy is still to be achieved. Although today's CNN have brought us closer to autonomy than ever before. CNN contain artificial neurons which are trained using preset rules and these rules determine whether it will provide an output or not when given a set of inputs. CNN will analyze various road footages, which include various scenarios such as collisions, empty roads, traffic, etc. CNN will analyze and send appropriate instructions to the car such as brake, accelerate, slow down, etc.
International journal of simulation: systems, science & technology, 2019
Intelligent Transportation System (ITS) is one of the attributes that describe smart cities. One of its functions is detection and classification of vehicles that pass through roadways. With this information, traffic management sectors can plan and implement road rules for the betterment of the traffic flow. Vision-based approaches and other methods, however, work only in ideal environment which make researchers find new ways on how limitations like occlusions, nighttime and camera angle can be solved. This paper demonstrates using a deep learning method to accurately detect and classify vehicles on urban roadways in a certain city. Additionally, a vehicle classifier was built and tested using a machine learning framework known as TensorFlow. Faster R-CNN model, with captured CCTV-video as dataset, was used to train the vehicle classifier. The performance of the newlytrained classifier has been evaluated using different classification metrics. Results show that using the proposed method, 93% accuracy and 78% F1-score in detecting and classifying vehicles were achieved based on labeled data. However, researchers also took note of the detection errors that showed during testing. Configurations in some steps has been provided to minimize such misclassifications. It was also recommended that the method be integrated as vital part of Intelligent Transportation Systems (ITS) in terms of vehicle detection and classification for future smart cities.
VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACH
Scientific Journal of Silesian University of Technology. Series Transport , 2021
We present vehicle detection classification using the Convolution Neural Network (CNN) of the deep learning approach. The automatic vehicle classification for traffic surveillance video systems is challenging for the Intelligent Transportation System (ITS) to build a smart city. In this article, three different vehicles: bike, car and truck classification are considered for around 3,000 bikes, 6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract different vehicle dataset's different features without a manual selection of features. The accuracy of CNN is measured in terms of the confidence values of the detected object. The highest confidence value is about 0.99 in the case of the bike category vehicle classification. The automatic vehicle classification supports building an electronic toll collection system and identifying emergency vehicles in the traffic.
sinkron
Vehicle identification and detection is an important part of building intelligent transportation. Various methods have been proposed in this field, but recently the YOLOv8 model has been proven to be one of the most accurate methods applied in various fields. In this study, we propose a YOLOv8 model approach for the identification and detection of 9 vehicle classes in a reprocessed image data set. The steps are carried out by adding labels to the dataset which consists of 2,042 image data for training, 204 validation images and 612 test data. From the results of the training, it produces an accuracy value of 77% with the setting of epoch = 100, batch = 8 and image size of 640. For testing, the YOLOv8 model can detect the type of vehicle on video assets recorded by vehicle activity at intersections with. However, the occlusion problem overlapping vehicle objects has a significant impact on the accuracy value, so it needs to be improved. In addition, the addition of image datasets and...
An Improved Deep Learning Solution for Object Detection in Self-Driving Cars
2020
Reliable object detection is one of the most important requirements of environment perception in autonomous driving. The goal of this research is to find a convenient solution to detect objects in images from the self-driving car medium. Convolutional neural networks (CNNs) are deep neural networks used in image processing, object classification, and object recognition. Therefore, deep convolution networks are employed in this project to identify objects accurately. In order to train and evaluate the neural network, we used BDD100K dataset which is one of the largest open-source datasets in autonomous driving published by Berkeley University. The approach used in the proposed algorithm is to apply the feature pyramid network along with a single-stage object detector, which enhances the accuracy of object detection. In addition, it improves the detection of different scales, especially small ones compared to those of the previous works, leading to increased safety and security in sel...
Image Segmentation and Object Detection for Automobile using OpenCV and CNN
Journal of Network and Information Security, 2024
Image segmentation and object detection using CNN (Convolutional Neural Network) and OpenCV (Open-Source Computer Vision) is a popular research area in the field of computer vision and autonomous driving. This method employs deep learning techniques and image processing algorithms to detect and track objects in real-time from a video stream captured by a camera mounted on a vehicle. The main aim of this project is to develop an accurate and robust object detection system that can detect various types of objects such as vehicles, pedestrians, and bicycles on the road. The proposed system uses a pre-trained CNN model to detect objects and OpenCV for further image processing and filtering. The system is evaluated on a publicly available dataset and achieves high accuracy and detection rates for various objects. The results of this study show the potential of using deep learning and image processing algorithms for real-time object detection in autonomous vehicles and traffic control systems. This study examines the use of Convolutional Neural Network techniques that have been used for image segmentation and object detection in road traffic. The study explored the use of Gaussian filters in image preprocessing. The study also trained the model to detect road traffic objects and return output/feedback. The experimental result of the model was an accuracy of 96% across 26 classes and a recall of 92%. The study, therefore, recommends the use of object detection models in road traffic systems and autonomous vehicles. Keywords: Artificial Intelligence (AI), Computer vision, Image segmentation, Object detection.