scholarly journals FastUAV-NET: A Multi-UAV Detection Algorithm for Embedded Platforms

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 724
Author(s):  
Amir Yavariabdi ◽  
Huseyin Kusetogullari ◽  
Turgay Celik ◽  
Hasan Cicek
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Computing Time ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Detection Accuracy ◽  
Video Streams ◽  
Global Features ◽  
Detection Algorithms ◽  
Multi Uav

In this paper, a real-time deep learning-based framework for detecting and tracking Unmanned Aerial Vehicles (UAVs) in video streams captured by a fixed-wing UAV is proposed. The proposed framework consists of two steps, namely intra-frame multi-UAV detection and the inter-frame multi-UAV tracking. In the detection step, a new multi-scale UAV detection Convolutional Neural Network (CNN) architecture based on a shallow version of You Only Look Once version 3 (YOLOv3-tiny) widened by Inception blocks is designed to extract local and global features from input video streams. Here, the widened multi-UAV detection network architecture is termed as FastUAV-NET and aims to improve UAV detection accuracy while preserving computing time of one-step deep detection algorithms in the context of UAV-UAV tracking. To detect UAVs, the FastUAV-NET architecture uses five inception units and adopts a feature pyramid network to detect UAVs. To obtain a high frame rate, the proposed method is applied to every nth frame and then the detected UAVs are tracked in intermediate frames using scalable Kernel Correlation Filter algorithm. The results on the generated UAV-UAV dataset illustrate that the proposed framework obtains 0.7916 average precision with 29 FPS performance on Jetson-TX2. The results imply that the widening of CNN network is a much more effective way than increasing the depth of CNN and leading to a good trade-off between accurate detection and real-time performance. The FastUAV-NET model will be publicly available to the research community to further advance multi-UAV-UAV detection algorithms.

scholarly journals Accurate and Lightweight RailNet for Real-Time Rail Line Detection

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2038
Author(s):  
Zhen Tao ◽  
Shiwei Ren ◽  
Yueting Shi ◽  
Xiaohua Wang ◽  
Weijiang Wang
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Social Progress ◽  
Line Detection ◽  
Detection Accuracy ◽  
Railway Transportation ◽  
Deep Convolutional Neural Networks ◽  
Detection Algorithms ◽  
Rail Line ◽  
Binary Segmentation

Railway transportation has always occupied an important position in daily life and social progress. In recent years, computer vision has made promising breakthroughs in intelligent transportation, providing new ideas for detecting rail lines. Yet the majority of rail line detection algorithms use traditional image processing to extract features, and their detection accuracy and instantaneity remain to be improved. This paper goes beyond the aforementioned limitations and proposes a rail line detection algorithm based on deep learning. First, an accurate and lightweight RailNet is designed, which takes full advantage of the powerful advanced semantic information extraction capabilities of deep convolutional neural networks to obtain high-level features of rail lines. The Segmentation Soul (SS) module is creatively added to the RailNet structure, which improves segmentation performance without any additional inference time. The Depth Wise Convolution (DWconv) is introduced in the RailNet to reduce the number of network parameters and eventually ensure real-time detection. Afterward, according to the binary segmentation maps of RailNet output, we propose the rail line fitting algorithm based on sliding window detection and apply the inverse perspective transformation. Thus the polynomial functions and curvature of the rail lines are calculated, and rail lines are identified in the original images. Furthermore, we collect a real-world rail lines dataset, named RAWRail. The proposed algorithm has been fully validated on the RAWRail dataset, running at 74 FPS, and the accuracy reaches 98.6%, which is superior to the current rail line detection algorithms and shows powerful potential in real applications.

scholarly journals A Hyperspectral Anomaly Detection Algorithm Based on Morphological Profile and Attribute Filter with Band Selection and Automatic Determination of Maximum Area

2020 ◽  
Vol 12 (20) ◽  
pp. 3387
Author(s):  
Ferdi Andika ◽  
Mia Rizkinia ◽  
Masahiro Okuda
Keyword(s):  
Anomaly Detection ◽  
Computing Time ◽  
Area Under The Curve ◽  
Detection Algorithm ◽  
High Spectral Resolution ◽  
Band Selection ◽  
Detection Accuracy ◽  
Maximum Area ◽  
Detection Algorithms ◽  
Rich Information

Anomaly detection is one of the most challenging topics in hyperspectral imaging due to the high spectral resolution of the images and the lack of spatial and spectral information about the anomaly. In this paper, a novel hyperspectral anomaly detection method called morphological profile and attribute filter (MPAF) algorithm is proposed. Aiming to increase the detection accuracy and reduce computing time, it consists of three steps. First, select a band containing rich information for anomaly detection using a novel band selection algorithm based on entropy and histogram counts. Second, remove the background of the selected band with morphological profile. Third, filter the false anomalous pixels with attribute filter. A novel algorithm is also proposed in this paper to define the maximum area of anomalous objects. Experiments were run on real hyperspectral datasets to evaluate the performance, and analysis was also conducted to verify the contribution of each step of MPAF. The results show that the performance of MPAF yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), precision-recall, and computing time, i.e., 0.9916, 0.7055, and 0.25 s, respectively. Compared with four other anomaly detection algorithms, MPAF yielded the highest average AUC for ROC and precision-recall in eight out of thirteen and nine out of thirteen datasets, respectively. Further analysis also proved that each step of MPAF has its effectiveness in the detection performance.

scholarly journals Multi-Scale Safety Helmet Detection Based on SAS-YOLOv3-Tiny

2021 ◽  
Vol 11 (8) ◽  
pp. 3652
Author(s):  
Rao Cheng ◽  
Xiaowei He ◽  
Zhonglong Zheng ◽  
Zhentao Wang
Keyword(s):  
Detection Algorithm ◽  
Model Complexity ◽  
Detection Accuracy ◽  
Global Features ◽  
Original Algorithm ◽  
Scale Feature ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Local And Global Features ◽  
A Minor

In the practical application scenarios of safety helmet detection, the lightweight algorithm You Only Look Once (YOLO) v3-tiny is easy to be deployed in embedded devices because its number of parameters is small. However, its detection accuracy is relatively low, which is why it is not suitable for detecting multi-scale safety helmets. The safety helmet detection algorithm (named SAS-YOLOv3-tiny) is proposed in this paper to balance detection accuracy and model complexity. A light Sandglass-Residual (SR) module based on depthwise separable convolution and channel attention mechanism is constructed to replace the original convolution layer, and the convolution layer of stride two is used to replace the max-pooling layer for obtaining more informative features and promoting detection performance while reducing the number of parameters and computation. Instead of two-scale feature prediction, three-scale feature prediction is used here to improve the detection effect about small objects further. In addition, an improved spatial pyramid pooling (SPP) module is added to the feature extraction network to extract local and global features with rich semantic information. Complete-Intersection over Union (CIoU) loss is also introduced in this paper to improve the loss function for promoting positioning accuracy. The results on the self-built helmet dataset show that the improved algorithm is superior to the original algorithm. Compared with the original YOLOv3-tiny, the SAS-YOLOv3-tiny has significantly improved all metrics (including Precision (P), Recall (R), Mean Average Precision (mAP), F1) at the expense of only a minor speed while keeping fewer parameters and amounts of calculation. Meanwhile, the SAS-YOLOv3-tiny algorithm shows advantages in accuracy compared with lightweight object detection algorithms, and its speed is faster than the heavyweight model.

scholarly journals Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3646
Author(s):  
Jingwei Cao ◽  
Chuanxue Song ◽  
Silun Peng ◽  
Shixin Song ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Clustering Algorithm ◽  
Pedestrian Detection ◽  
Grid Cell ◽  
Detection Algorithm ◽  
Intelligent Vehicles ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Driving Assistance

Pedestrian detection is an important aspect of the development of intelligent vehicles. To address problems in which traditional pedestrian detection is susceptible to environmental factors and are unable to meet the requirements of accuracy in real time, this study proposes a pedestrian detection algorithm for intelligent vehicles in complex scenarios. YOLOv3 is one of the deep learning-based object detection algorithms with good performance at present. In this article, the basic principle of YOLOv3 is elaborated and analyzed firstly to determine its limitations in pedestrian detection. Then, on the basis of the original YOLOv3 network model, many improvements are made, including modifying grid cell size, adopting improved k-means clustering algorithm, improving multi-scale bounding box prediction based on receptive field, and using Soft-NMS algorithm. Finally, based on INRIA person and PASCAL VOC 2012 datasets, pedestrian detection experiments are conducted to test the performance of the algorithm in various complex scenarios. The experimental results show that the mean Average Precision (mAP) value reaches 90.42%, and the average processing time of each frame is 9.6 ms. Compared with other detection algorithms, the proposed algorithm exhibits accuracy and real-time performance together, good robustness and anti-interference ability in complex scenarios, strong generalization ability, high network stability, and detection accuracy and detection speed have been markedly improved. Such improvements are significant in protecting the road safety of pedestrians and reducing traffic accidents, and are conducive to ensuring the steady development of the technological level of intelligent vehicle driving assistance.

scholarly journals A web-based application for face detection in real-time images and videos

2022 ◽  
Vol 2161 (1) ◽  
pp. 012071
Author(s):  
Mehul Arora ◽  
Sarthak Naithani ◽  
Anu Shaju Areeckal
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Face Detection ◽  
User Interfaces ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Web Based ◽  
Detection Algorithms ◽  
Haar Cascade ◽  
The Web

Abstract Face detection is widely used in the consumer industry such as advertising, user interfaces, video streaming apps and in many security applications. Every application has its own demands and constraints, and hence cannot be fulfilled by a single face detection algorithm. In this work, we developed an interactive web-based application for face detection in real-time images and videos. Pretrained face detection algorithms, namely Haar cascade classifier, HOG-based frontal face detector, Multi-task Cascaded Convolutional Neural Network (MTCNN) and Deep Neural Network (DNN), were used in the web-based application. A performance analysis of these face detection algorithms is done for various parameters such as different lighting conditions, face occlusion and frame rate. The web app interface can be used for an easy comparison of different face detection algorithms. This will help the user to decide on the algorithm that suits their purpose and requirements for various applications.

scholarly journals Distant Measurement of Plethysmographic Signal in Various Lighting Conditions Using Configurable Frame-Rate Camera

2016 ◽  
Vol 23 (4) ◽  
pp. 579-592 ◽  
Author(s):  
Jaromir Przybyło ◽  
Eliasz Kańtoch ◽  
Mirosław Jabłoński ◽  
Piotr Augustyniak
Keyword(s):  
Heart Rate ◽  
Assisted Living ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Infrared Light ◽  
Fluorescent Light ◽  
Detection Accuracy ◽  
Rate Measurement ◽  
Lighting Conditions ◽  
Heart Rate Measurement

Abstract Videoplethysmography is currently recognized as a promising noninvasive heart rate measurement method advantageous for ubiquitous monitoring of humans in natural living conditions. Although the method is considered for application in several areas including telemedicine, sports and assisted living, its dependence on lighting conditions and camera performance is still not investigated enough. In this paper we report on research of various image acquisition aspects including the lighting spectrum, frame rate and compression. In the experimental part, we recorded five video sequences in various lighting conditions (fluorescent artificial light, dim daylight, infrared light, incandescent light bulb) using a programmable frame rate camera and a pulse oximeter as the reference. For a video sequence-based heart rate measurement we implemented a pulse detection algorithm based on the power spectral density, estimated using Welch’s technique. The results showed that lighting conditions and selected video camera settings including compression and the sampling frequency influence the heart rate detection accuracy. The average heart rate error also varies from 0.35 beats per minute (bpm) for fluorescent light to 6.6 bpm for dim daylight.

scholarly journals Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhaoli Wu ◽  
Xin Wang ◽  
Chao Chen
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Pedestrian Detection ◽  
Infrared Image ◽  
Far Infrared ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Detection Model ◽  
Embedded Platform

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

scholarly journals AN OPTIONAL THRESHOLD WITH SVM CLOUD DETECTION ALGORITHM AND DSP IMPLEMENTATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 771-777 ◽  
Author(s):  
Guoqing Zhou ◽  
Xiang Zhou ◽  
Tao Yue ◽  
Yilong Liu
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Threshold Method ◽  
Dsp Implementation ◽  
Svm Algorithm

This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.

scholarly journals Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Yicheng Li ◽  
Long Chen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Depth Information ◽  
Detection Accuracy ◽  
Classification Rate

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

scholarly journals Research on a Fast Human-Detection Algorithm for Unmanned Surveillance Area in Bulk Ports

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Chao Mi ◽  
Xin He ◽  
Haiwei Liu ◽  
Youfang Huang ◽  
Weijian Mi
Keyword(s):  
Real Time ◽  
Computing Time ◽  
Training Sample ◽  
Detection Algorithm ◽  
Human Detection ◽  
Security Requirements ◽  
Support Vector ◽  
Svm Classifier ◽  
Histograms Of Oriented Gradients ◽  
Heavy Equipment

With the development of port automation, most operational fields utilizing heavy equipment have gradually become unmanned. It is therefore imperative to monitor these fields in an effective and real-time manner. In this paper, a fast human-detection algorithm is proposed based on image processing. To speed up the detection process, the optimized histograms of oriented gradients (HOG) algorithm that can avoid the large number of double calculations of the original HOG and ignore insignificant features is used to describe the contour of the human body in real time. Based on the HOG features, using a training sample set consisting of scene images of a bulk port, a support vector machine (SVM) classifier combined with the AdaBoost classifier is trained to detect human. Finally, the results of the human detection experiments on Tianjin Port show that the accuracy of the proposed optimized algorithm has roughly the same accuracy as a traditional algorithm, while the proposed algorithm only takes 1/7 the amount of time. The accuracy and computing time of the proposed fast human-detection algorithm were verified to meet the security requirements of unmanned port areas.

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