Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

Hung, Goon Li; Sahimi, Mohamad Safwan Bin; Samma, Hussein; Almohamad, Tarik Adnan; Lahasan, Badr

doi:10.1007/s42979-020-00125-y

Cited by 35 publications

(20 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For energy conservation and pollution reduction, the solution of blast furnace slag smelting problem is more inclined to use the theoretical model to predict the melting behavior in the blast furnace slag. Convolutional neural network (CNN) [31]- [34], region convolutional neural network (R-CNN) [35]- [37], fast R-CNN and support vector machine (SVM) [38], [39] have become popular in the field of target tracking and positioning [40], [41]. CNN and other methods have been used to extract the depth features and adaptively fuse them [42].…”

Section: Introductionmentioning

confidence: 99%

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Zhu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This paper focuses on how to perspective the melting behavior of solid iron tailings in molten blast furnace slag and take a new non-contact visual analytical method to predict its melting law. The optimized convolution neural network (CNN) is used to track the moving target in charge coupled device (CCD) camera system efficiently and accurately, and the melting behavior of SiO 2 is described by coordinate translation transformation theory. Hierarchical agglomerative clustering (HAC) and delaunay triangulation were used to extract the characteristic parameters of the melting process of SiO 2. The prediction model of the melting rate of SiO 2 at high temperature was established by least square fitting (LSF) and dimensional analysis, and compared with the actual melting rate of SiO 2 obtained by experiments. The results show that the melting characteristics of SiO 2 at high temperature are in accord with certain function rule. The performance of optimized CNN in terms of processing time and accuracy is significantly improved, and the fusion rate prediction model of SiO 2 is verified by 100% accuracy. It provides theoretical support and model basis for the improvement of slag cotton preparation technology. INDEX TERMS Melting rate, target tracking, feature extraction, dimensional analysis, best match.

show abstract

Section: Introductionmentioning

confidence: 99%

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Zhu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The faster R-CNN algorithm proposed by Ren Shaoqing is famous for its efficient detection, and other scholars have proposed an improved algorithm based on the faster R-CNN algorithm. The implementation process of the faster R-CNN algorithm is shown in Figure 1 [ 9 , 10 ].…”

Section: Faster R-cnn Modelmentioning

confidence: 99%

Detection of Aerobics Action Based on Convolutional Neural Network

Zhang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

To further improve the accuracy of aerobics action detection, a method of aerobics action detection based on improving multiscale characteristics is proposed. In this method, based on faster R-CNN and aiming at the problems existing in faster R-CNN, the feature pyramid network (FPN) is used to extract aerobics action image features. So, the low-level semantic information in the images can be extracted, and it can be converted into high-resolution deep-level semantic information. Finally, the target detector is constructed by the above-extracted anchor points so as to realize the detection of aerobics action. The results show that the loss function of the neural network is reduced to 0.2 by using the proposed method, and the accuracy of the proposed method can reach 96.5% compared with other methods, which proves the feasibility of this study.

show abstract

“…This section includes the use of different techniques and deep learning models for the detection of different objects, among which one research by [17] focuses on the smoke produced by Gunfire to detect the location of the fired Gun. Other research work done by [18] includes using the Faster-RCNN model to detect objects and pedestrians. Support vector machine (SVM) was used by [19] to do real-time clothing recognition from surveillance videos.…”

Section: Irregular Shaped Object Detection and Supporting Literaturementioning

confidence: 99%

Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

Ashraf¹,

Imran²,

Qahtani³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos. This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter. The proposed framework is based on You Only Look Once (YOLO) and Area of Interest (AOI). Initially, the models take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm. The proposed architecture will be assessed through various performance parameters such as False Negative, False Positive, precision, recall rate, and F1 score. The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved. Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN. It is promising to be used in the field of security and weapon detection.

show abstract

Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

Cited by 35 publications

References 17 publications

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Detection of Aerobics Action Based on Convolutional Neural Network

Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

Contact Info

Product

Resources

About