Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study

Espinosa, Jorge E.; Velastín, Sergio A.; Branch, John W.

doi:10.1007/978-3-319-70010-6_1

Cited by 35 publications

(12 citation statements)

References 49 publications

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“…Explaining The faster RCNN architecture was proposed to reduce the running time of the object detection in a given image. The Regional Proposal Network (RPN) is in the heart of the Faster RCNN structure [24,25]. RPN is composed of four layers such as input layer, region proposal layer, feature extraction layer and classification layer, respectively.…”

Section: Research Methods 31 Faster Region With Convolutional Neural Networkmentioning

confidence: 99%

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Fused faster RCNNs for efficient detection of the license plates

Omar

Abdulazeez

Şengür

et al. 2020

IJEECS

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Automatic License Plate Detection and Recognition (ALPD-R) is an important and challenging application for traffic surveillance, traffic safety, security, services purposes and parking management. Generally, traditional image processing routines have been used in ALPD-R. Although the general approaches perform well on ALPD-R, new and efficient approaches are needed to improve the detection accuracies. Thus, in this paper, a new approach, which is based on fusing of multiple Faster Regions with Convolutional Neutral Network (Faster- RCNN) architectures, is proposed. More specially, the Deep Learning (DL) is used to detect license plates in given images. The proposed license plate detection method uses three Faster- RCNN modules where each faster RCNN module uses a pre-trained CNN model namely AlexNet, VGG16 and VGG19. Each Faster-RCNN module is trained independently and their results are fused in fusing layer. Fusing layer use average operator on the X and Y coordinates of the outputs of the Faster-RCNN modules and maximum operator is employed on the width and height outputs of the Faster-RCNN modules. A publicly available dataset is used in experiments. The accuracy is used as a performance indicator of the proposed method. For 100 testing images, the proposed method detects the exact location of license plates for 97 images. The accuracy of the proposed method is 97%.

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Section: Research Methods 31 Faster Region With Convolutional Neural Networkmentioning

confidence: 99%

“…Region proposal network (RPN) aims to generate potential regions and it employs a network to determine if the potential regions contain any objects [24,25]. The region proposals are generated by the selective search algorithm.…”

Section: Region Proposal Network (Rpn)mentioning

confidence: 99%

Fused faster RCNNs for efficient detection of the license plates

Omar

Abdulazeez

Şengür

et al. 2020

IJEECS

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“…Some methods perform background subtraction for object individualization, then CNN is applied to extract features from the detected moving objects, these features are later used for classification. Such a method is reported for vehicle classification in [73], applying the same strategy for feature extraction in [74], using AlexNet [52] as a feature extractor for motorcycle classification in urban scenarios. Features extracted by the CNN model are then classified by a linear SVM, reaching an almost perfect accuracy on classification (albeit in a small dataset).…”

Section: Other Approachesmentioning

confidence: 99%

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Espinosa

Velastín

Branch

2021

IEEE Trans. Intell. Transport. Syst.

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Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area.

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“…The work of Vishnu et al [32] use Convolutional Neural Networks (CNNs) as feature extractors in combination with background subtraction for object detection. Once the object is detected, for instance using GMM, the features extracted using the CNN model (e.g., AlexNet), are used to perform classification [33]. Instead of background subtraction, object localisation uses selective search as in [34].…”

Section: Deep Learning For Motorcycle Detectionmentioning

confidence: 99%

EspiNet V2: a region based deep learning model for detecting motorcycles in urban scenarios

Oviedo

Velastín

Bedoya

2019

DYNA

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This paper presents “EspiNet V2” a Deep Learning model, based on the region-based detector Faster R-CNN. The model is used for the detection of motorcycles in urban environments, where occlusion is likely. For training, two datasets are used: the Urban Motorbike Dataset (UMD-10K) of 10,000 annotated images, and the new SMMD (Secretaría de Movilidad Motorbike Dataset), of 5,000 images captured from the Traffic Control CCTV System in Medellín (Colombia). Results achieved on the UMD-10K dataset reach 88.8% in average precision (AP) even when 60% motorcycles were occluded, and the images were captured from a low angle and a moving camera. Meanwhile, an AP of 79.5% is reached for SSMD. EspiNet V2 outperforms popular models such as YOLO V3 and Faster R-CNN (VGG16 based) trained end-to-end for those datasets

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Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study

Cited by 35 publications

References 49 publications

Fused faster RCNNs for efficient detection of the license plates

Fused faster RCNNs for efficient detection of the license plates

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

EspiNet V2: a region based deep learning model for detecting motorcycles in urban scenarios

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