Object detection in real time based on improved single shot multi-box detector algorithm

Kumar, Ashwani; Zhang, Zuopeng; Lyu, Hongbo

doi:10.1186/s13638-020-01826-x

Cited by 117 publications

(33 citation statements)

References 27 publications

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“…YOLOv3 is an improved version of YOLOv2, featuring a new feature extraction network, a better backbone classifier, and multiscale prediction. Although Kumar et al [12] suggested a two-stage detector with high object detection accuracy, it is limited for video surveillance due to sluggish real-time inference speed. Although Morera et al [13] suggested YOLOv3, it achieved the same classification accuracy as a single-shot detector (SSD).…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

Ali

Shanmugam²,

Muthusamy³

et al. 2022

Electronics

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The COVID-19 pandemic disrupted people’s livelihoods and hindered global trade and transportation. During the COVID-19 pandemic, the World Health Organization mandated that masks be worn to protect against this deadly virus. Protecting one’s face with a mask has become the standard. Many public service providers will encourage clients to wear masks properly in the foreseeable future. On the other hand, monitoring the individuals while standing alone in one location is exhausting. This paper offers a solution based on deep learning for identifying masks worn over faces in public places to minimize the coronavirus community transmission. The main contribution of the proposed work is the development of a real-time system for determining whether the person on a webcam is wearing a mask or not. The ensemble method makes it easier to achieve high accuracy and makes considerable strides toward enhancing detection speed. In addition, the implementation of transfer learning on pretrained models and stringent testing on an objective dataset led to the development of a highly dependable and inexpensive solution. The findings provide validity to the application’s potential for use in real-world settings, contributing to the reduction in pandemic transmission. Compared to the existing methodologies, the proposed method delivers improved accuracy, specificity, precision, recall, and F-measure performance in three-class outputs. These metrics include accuracy, specificity, precision, and recall. An appropriate balance is kept between the number of necessary parameters and the time needed to conclude the various models.

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Section: Literature Reviewmentioning

confidence: 99%

Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

Ali

Shanmugam²,

Muthusamy³

et al. 2022

Electronics

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“…It aims to identify object instances in natural images from a wide range of predefined categories [6]. Object detection techniques are analyzed [7] to detect objects in real-time on any system running the proposed model in any area. The proposed method employs multi-layer convolutional neural networks to create a multi-layer system model that can classify given objects into specified classes.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Deep Learning models on UV ink : a Fake Money detection scheme with RPN

Chowdhury

Das

Karmaker

2021

Preprint

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As soon as coins or money was invented, there were people trying to make counterfeits. Counterfeit money is fake money that is produced without the permission of the state or government, usually to imitate the currency and deceive the intended recipient. In Bangladesh, this is a significant problem and the problem is becoming more and more phenomenon as the days are passing by. Today’s modern bank notes have several security features that makes easier to identify fake notes. One of the security features is the use of UV ink. Bank notes deliberately put random flecks of color scattered all over the surface of the money - which acts as a extra layer of protection against counterfeiters. We propose an automatic authentication model for identifying counterfeit money based on these random flecks of color which is visible under UV light. To obtain a benchmark result, existing object detection pre-trained models were used, followed by MobileNet, Inception, ResNet50, ResNet101, and Inception-ResNet architectures. After that, using the Region Proposal Network (RPN) method with Convolutional Neural Network (CNN) based classification the optimal model was proposed. The proposed model had a 96.3 percent accuracy. It is critical to reduce the circulation of counterfeit money in a country’s economy to stop inflation. This study will aid in the detection of counterfeit money and, hopefully, reduce its spread.

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“…In the object detection network without feature information fusion, one type of prediction is based on single-layer feature map, such as two-stage methods (Fast RCNN [16], Faster R-CNN [17]), single-stage methods YOLO (You Only Look Once) [18] and YOLOv2 [19]. The other is to predict on multiple feature graphs, such as SSD (single-shot multi-box detector) [20] and MS-CNN (multi-scale CNN) [21]. In the object detection network with feature information fusion, one kind of prediction is based on a single fused feature graph.…”

Section: Related Workmentioning

confidence: 99%

Multi-scale information fusion based on convolution kernel pyramid and dilated convolution for Wushu moving object detection

Li¹

2018

ICST Transactions on Scalable Information Systems

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In complex background, the accuracy of moving object detection can be affected by some factors such as illumination change, short occlusion and background movement. This paper proposes a new multi-scale information fusion based on convolution kernel pyramid and dilated convolution for Wushu moving object detection. The proposed model uses a variety of ways to fuse the feature information. First, the multi-layer feature map information with different sizes is fused by the per-pixel addition method. Then the feature map of different stages is splicing in the channel dimension to form the information fusion feature layer with rich semantic information and detail information as the prediction layer of the model. In this model, convolution kernel pyramid structure is introduced into the anchor frame mechanism to solve the multi-scale problem of detecting objects. The number of parameters increased by large convolution kernel is reduced by using dilated convolution to reduce the number of anchor frames reasonably. Experimental results show that the proposed fusion algorithm has certain anti-interference ability and high precision for moving object detection in complex environment compared the state-of-the-art methods.

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Object detection in real time based on improved single shot multi-box detector algorithm

Cited by 117 publications

References 27 publications

Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

Evaluation of Deep Learning models on UV ink : a Fake Money detection scheme with RPN

Multi-scale information fusion based on convolution kernel pyramid and dilated convolution for Wushu moving object detection

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