A Robust Vehicle Detection Approach based on Faster R-CNN Algorithm

Tourani, Ali; Soroori, Sajjad; Shahbahrami, Asadollah; Khazaee, Saeed; Akoushideh, Alireza

doi:10.1109/pria.2019.8785988

Cited by 15 publications

(5 citation statements)

References 15 publications

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“…SSD strikes a balance between speed and accuracy and is also a popular choice for vehicle detection [38]. On the other hand, Faster R-CNN is also known for its accuracy and is commonly used for tasks such as object tracking because of its ability to detect occluded objects [39]. It uses a region proposal network to generate potential object locations, which allows it to detect objects even when they are partially occluded or obscured by other objects in the scene [40], which is extremely useful in our vehicle parking application.…”

Section: Related Workmentioning

confidence: 99%

A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods

Lakshmanan,

Roach,

Giannetti

et al. 2023

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Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world model for vehicle detection and classification from single-beam LiDAR of a roadside parking scenario. The paper presents a synthetically augmented transfer learning approach for LiDAR-based vehicle detection and the implementation of synthetic LiDAR data. A synthetic augmented transfer learning method was used to supplement the small real-world data set and allow the development of data-handling techniques. In addition, adding the synthetically augmented transfer learning method increases the robustness and overall accuracy of the model. Experiments show that the method can be used for fast deployment of the model for vehicle detection using a LIDAR sensor.

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Section: Related Workmentioning

confidence: 99%

A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods

Lakshmanan,

Roach,

Giannetti

et al. 2023

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“…A myriad of DNNs have been proposed for vehicle detection. Faster R-CNN was applied on video frames to get the desired accuracy of vehicle detection in road scene perception [38], though the algorithm has a limitation in the speed of inference. Cao et al [17] improved the basic structure of SSD by adding inception blocks and feature fusion layers in the original network to detect tiny objects accurately.…”

Section: Vehicle Detectionmentioning

confidence: 99%

Flexible neural network for fast and accurate road scene perception

Mehtab

Yan

2022

Multimed Tools Appl

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Accurate object detection on the road is the most important requirement of autonomous vehicles. Extensive work has been accomplished for car, pedestrian, and cyclist detection; however, comparatively, very few efforts have been put into 2D object detection. In this article, a dynamic approach is investigated to design a perfect unified neural network that could achieve the best results based on our available hardware. The proposed architecture is based on CSPNet for feature extraction in an end-to-end way. The net extracts visual features by using backbone subnet, visual object detection is based on a feature pyramid network (FPN). In order to increase the net flexibility, an auto-anchor generating method is applied to the detection layer that makes the net suitable for any datasets. For fine-tuning the net, activation, optimization, and loss functions are considered along with multiple check points. The proposed net is trained and tested based on the benchmark KITTI dataset. Our extensive experiments show that the proposed model for visual object detection is superior to others, where other nets output very low accuracy for pedestrian and cyclist detection, our proposed model achieves 99.3% recall rate based on our dataset.

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“…The authors of this paper have contributed to many computer vision and machine learning projects and proposed various approaches in the field of ITS. Some of these approaches include vehicle count using video processing [16], deep learning-based vehicle detection [17], vehicle speed measurement [18][19], license plate localization [8,20], and Farsi character recognition [8]. Accordingly, we claim that we have felt the essence of reliable data for the development of domestic robust applications for Fig.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Iranis: A Large-scale Dataset of Iranian Vehicles License Plate Characters

Tourani

Soroori

Shahbahrami

et al. 2021

2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)

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Providing huge amounts of data is a fundamental demand when dealing with Deep Neural Networks (DNNs). Employing these algorithms to solve computer vision problems resulted in the advent of various image datasets to feed the most common visual imagery deep structures, known as Convolutional Neural Networks (CNNs). In this regard, some datasets can be found that contain hundreds or even thousands of images for license plate detection and optical character recognition purposes. However, no publicly available image dataset provides such data for the recognition of Farsi characters used in car license plates. The gap has to be filled due to the numerous advantages of developing accurate deep learning-based systems for law enforcement and surveillance purposes. This paper introduces a large-scale dataset that includes images of numbers and characters used in Iranian car license plates. The dataset, named Iranis, contains more than 83,000 images of Farsi numbers and letters collected from real-world license plate images captured by various cameras. The variety of instances in terms of camera shooting angle, illumination, resolution, and contrast make the dataset a proper choice for training DNNs. Dataset images are manually annotated for object detection and image classification. Finally, and to build a baseline for Farsi character recognition, the paper provides a performance analysis using a YOLO v.3 object detector.

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A Robust Vehicle Detection Approach based on Faster R-CNN Algorithm

Cited by 15 publications

References 15 publications

A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods

A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods

Flexible neural network for fast and accurate road scene perception

Iranis: A Large-scale Dataset of Iranian Vehicles License Plate Characters

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