scholarly journals Ship Detection and Tracking in Inland Waterways Using Improved YOLOv3 and Deep SORT

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 308
Author(s):  
Yang Jie ◽  
LilianAsimwe Leonidas ◽  
Farhan Mumtaz ◽  
Munsif Ali
Keyword(s):  
Clustering Algorithm ◽  
Mutual Exclusion ◽  
Detection Algorithm ◽  
Inland Waterways ◽  
Softmax Classifier ◽  
Inland Navigation ◽  
Ship Detection ◽  
Detection And Tracking ◽  
Complex Scenes ◽  
Real Time Tracking

Ship detection and tracking is an important task in video surveillance in inland waterways. However, ships in inland navigation are faced with accidents such as collisions. For collision avoidance, we should strengthen the monitoring of navigation and the robustness of the entire system. Hence, this paper presents ship detection and tracking of ships using the improved You Only Look Once version 3 (YOLOv3) detection algorithm and Deep Simple Online and Real-time Tracking (Deep SORT) tracking algorithm. Three improvements are made to the YOLOv3 target detection algorithm. Firstly, the Kmeans clustering algorithm is used to optimize the initial value of the anchor frame to make it more suitable for ship application scenarios. Secondly, the output classifier is modified to a single Softmax classifier to suit our ship dataset which has three ship categories and mutual exclusion. Finally, Soft Non-Maximum Suppression (Soft-NMS) is introduced to solve the deficiencies of the Non-Maximum Suppression (NMS) algorithm when screening candidate frames. Results showed the mean Average Precision (mAP) and Frame Per Second (FPS) of the improved algorithm are increased by about 5% and 2, respectively, compared with the existing YOLOv3 detecting Algorithm. Then the improved YOLOv3 is applied in Deep Sort and the performance result of Deep Sort showed that, it has greater performance in complex scenes, and is robust to interference such as occlusion and camera movement, compared to state of art algorithms such as KCF, MIL, MOSSE, TLD, and Median Flow. With this improvement, it will help in the safety of inland navigation and protection from collisions and accidents.

scholarly journals Real-time embedded object detection and tracking system in Zynq SoC

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qingbo Ji ◽  
Chong Dai ◽  
Changbo Hou ◽  
Xun Li
Keyword(s):  
Real Time ◽  
Single Shot ◽  
Correlation Filters ◽  
Detection Model ◽  
Embedded Platform ◽  
Time Performance ◽  
Detection And Tracking ◽  
Complex Scenes ◽  
Real Time Tracking ◽  
Kernel Correlation

AbstractWith the increasing application of computer vision technology in autonomous driving, robot, and other mobile devices, more and more attention has been paid to the implementation of target detection and tracking algorithms on embedded platforms. The real-time performance and robustness of algorithms are two hot research topics and challenges in this field. In order to solve the problems of poor real-time tracking performance of embedded systems using convolutional neural networks and low robustness of tracking algorithms for complex scenes, this paper proposes a fast and accurate real-time video detection and tracking algorithm suitable for embedded systems. The algorithm combines the object detection model of single-shot multibox detection in deep convolution networks and the kernel correlation filters tracking algorithm, what is more, it accelerates the single-shot multibox detection model using field-programmable gate arrays, which satisfies the real-time performance of the algorithm on the embedded platform. To solve the problem of model contamination after the kernel correlation filters algorithm fails to track in complex scenes, an improvement in the validity detection mechanism of tracking results is proposed that solves the problem of the traditional kernel correlation filters algorithm not being able to robustly track for a long time. In order to solve the problem that the missed rate of the single-shot multibox detection model is high under the conditions of motion blur or illumination variation, a strategy to reduce missed rate is proposed that effectively reduces the missed detection. The experimental results on the embedded platform show that the algorithm can achieve real-time tracking of the object in the video and can automatically reposition the object to continue tracking after the object tracking fails.

scholarly journals High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

2021 ◽  
Vol 13 (10) ◽  
pp. 1909
Author(s):  
Jiahuan Jiang ◽  
Xiongjun Fu ◽  
Rui Qin ◽  
Xiaoyan Wang ◽  
Zhifeng Ma
Keyword(s):  
Deep Learning ◽  
Gpu Computing ◽  
Hot Spot ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Sar Image ◽  
Marine Monitoring ◽  
Ship Detection

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

scholarly journals Community Detection Based on Graph Representation Learning in Evolutionary Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4497
Author(s):  
Dongming Chen ◽  
Mingshuo Nie ◽  
Jie Wang ◽  
Yun Kong ◽  
Dongqi Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Network Structure ◽  
Clustering Algorithm ◽  
Laplacian Matrix ◽  
Representation Learning ◽  
Detection Algorithm ◽  
Graph Representation ◽  
Time Slice ◽  
Current Time ◽  
Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

scholarly journals Pedestrian detection algorithm based on improved muti-scale feature fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012008
Author(s):  
Hui Liu ◽  
Keyang Cheng
Keyword(s):  
Clustering Algorithm ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Data Sets ◽  
False Detection ◽  
Scale Feature ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Small Targets

Abstract Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.

Detection and Tracking Cows by Computer Vision and Image Classification Methods

2021 ◽  
Vol 13 (1) ◽  
pp. 1-45
Author(s):  
Terry Gao
Keyword(s):  
Feature Fusion ◽  
Binary Classification ◽  
Classification Problem ◽  
Detection Algorithm ◽  
Video Sequences ◽  
Image Block ◽  
Body Contour ◽  
Adaboost Algorithm ◽  
Target Model ◽  
Detection And Tracking

In this paper, the cow recognition and traction in video sequences is studied. In the recognition phase, this paper does some discussion and analysis which aim at different classification algorithms and feature extraction algorithms, and cow's detection is transformed into a binary classification problem. The detection method extracts cow's features using a method of multiple feature fusion. These features include edge characters which reflects the cow body contour, grey value, and spatial position relationship. In addition, the algorithm detects the cow body through the classifier which is trained by Gentle Adaboost algorithm. Experiments show that the method has good detection performance when the target has deformation or the contrast between target and background is low. Compared with the general target detection algorithm, this method reduces the miss rate and the detection precision is improved. Detection rate can reach 97.3%. In traction phase, the popular compressive tracking (CT) algorithm is proposed. The learning rate is changed through adaptively calculating the pap distance of image block. Moreover, the update for target model is stopped to avoid introducing error and noise when the classification response values are negative. The experiment results show that the improved tracking algorithm can effectively solve the target model update by mistaken when there are large covers or the attitude is changed frequently. For the detection and tracking of cow body, a detection and tracking framework for the image of cow is built and the detector is combined with the tracking framework. The algorithm test for some video sequences under the complex environment indicates the detection algorithm based on improved compressed perception shows good tracking effect in the changing and complicated background.

scholarly journals Visualization and analysis of SCImago Journal & Country Rank structure via journal clustering

2016 ◽  
Vol 68 (5) ◽  
pp. 607-627 ◽  
Author(s):  
Antonio J. Gómez-Núñez ◽  
Benjamin Vargas-Quesada ◽  
Zaida Chinchilla-Rodríguez ◽  
Vladimir Batagelj ◽  
Félix Moya-Anegón
Keyword(s):  
Clustering Algorithm ◽  
Citation Network ◽  
Detection Algorithm ◽  
Bibliographic Coupling ◽  
Content Type ◽  
Alternative Approach ◽  
Level Of Aggregation ◽  
Direct Citation ◽  
Community Detection Algorithm ◽  
Visualization Techniques

Purpose The purpose of this paper is to visualize the structure of SCImago Journal & Country Rank (SJR) coverage of the extensive citation network of Scopus journals, examining this bibliometric portal through an alternative approach, applying clustering and visualization techniques to a combination of citation-based links. Design/methodology/approach Three SJR journal-journal networks containing direct citation, co-citation and bibliographic coupling links are built. The three networks were then combined into a new one by summing up their values, which were later normalized through geo-normalization measure. Finally, the VOS clustering algorithm was executed and the journal clusters obtained were labeled using original SJR category tags and significant words from journal titles. Findings The resultant scientogram displays the SJR structure through a set of communities equivalent to SJR categories that represent the subject contents of the journals they cover. A higher level of aggregation by areas provides a broad view of the SJR structure, facilitating its analysis and visualization at the same time. Originality/value This is the first study using Persson’s combination of most popular citation-based links (direct citation, co-citation and bibliographic coupling) in order to develop a scientogram based on Scopus journals from SJR. The integration of the three measures along with performance of the VOS community detection algorithm gave a balanced set of clusters. The resulting scientogram is useful for assessing and validating previous classifications as well as for information retrieval and domain analysis.

Research on Traffic Sign Detection Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 3304-3308
Author(s):  
Mei Hua Xu ◽  
Yi Da Liu ◽  
Chen Jun Xia
Keyword(s):  
Clustering Algorithm ◽  
Color Image ◽  
Ccd Camera ◽  
Detection Algorithm ◽  
Traffic Sign ◽  
The Road ◽  
Sign Detection ◽  
Image Separation ◽  
Series Of Experiments ◽  
Traffic Sign Detection

As an important part of Advanced Driver Assistance System (ADAS), the traffic sign detection has been paid more and more attention. This paper studied and implemented a valid algorithm of traffic sign detection. Using K-means clustering algorithm to complete the image separation and extraction of prohibition signs from the RGB color image, and then matching them with templates to realize the detection of traffic signs by SIFT algorithm. Series of experiments for traffic sign detection have been carried out to prove the validity and correctness of the algorithm on the basis of the road images in front of the vehicle collected by CCD camera.

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