scholarly journals Automatic Detection and Segmentation on Gas Plumes from Multibeam Water Column Images

2020 ◽  
Vol 12 (18) ◽  
pp. 3085
Author(s):  
Jianhu Zhao ◽  
Dongxin Mai ◽  
Hongmei Zhang ◽  
Shiqi Wang
Keyword(s):  
Water Column ◽  
Local Binary Patterns ◽  
Recall Rate ◽  
Detection Accuracy ◽  
Localization Algorithm ◽  
Segmentation Method ◽  
Experiment Verification ◽  
Histogram Similarity ◽  
Time Segmentation ◽  
Hydrate Reservoirs

The detection of gas plumes from multibeam water column (MWC) data is the most direct way to discover gas hydrate reservoirs, but current methods often have low reliability, leading to inefficient detections. Therefore, this paper proposes an automatic method for gas plume detection and segmentation by analyzing the characteristics of gas plumes in MWC images. This method is based on the AdaBoost cascade classifier, combining the Haar-like feature and Local Binary Patterns (LBP) feature. After obtaining the detected result from the above algorithm, a target localization algorithm, based on a histogram similarity calculation, is given to exactly localize the detected target boxes, by considering the differences in gas plume and background noise in the backscatter strength. On this basis, a real-time segmentation method is put forward to get the size of the detected gas plumes, by integration of the image intersection and subtraction operation. Through the shallow-water and deep-water experiment verification, the detection accuracy of this method reaches 95.8%, the precision reaches 99.35% and the recall rate reaches 82.7%. Integrated with principles and experiments, the performance of the proposed method is analyzed and discussed, and finally some conclusions are drawn.

scholarly journals Deep-Learning-Based Pupil Center Detection and Tracking Technology for Visible-Light Wearable Gaze Tracking Devices

2021 ◽  
Vol 11 (2) ◽  
pp. 851
Author(s):  
Wei-Liang Ou ◽  
Tzu-Ling Kuo ◽  
Chin-Chieh Chang ◽  
Chih-Peng Fan
Keyword(s):  
Deep Learning ◽  
Visible Light ◽  
Tracking System ◽  
Recall Rate ◽  
Detection Accuracy ◽  
Learning Technology ◽  
Tracking Errors ◽  
Gaze Tracking ◽  
Detection Technology ◽  
Pupil Tracking

In this study, for the application of visible-light wearable eye trackers, a pupil tracking methodology based on deep-learning technology is developed. By applying deep-learning object detection technology based on the You Only Look Once (YOLO) model, the proposed pupil tracking method can effectively estimate and predict the center of the pupil in the visible-light mode. By using the developed YOLOv3-tiny-based model to test the pupil tracking performance, the detection accuracy is as high as 80%, and the recall rate is close to 83%. In addition, the average visible-light pupil tracking errors of the proposed YOLO-based deep-learning design are smaller than 2 pixels for the training mode and 5 pixels for the cross-person test, which are much smaller than those of the previous ellipse fitting design without using deep-learning technology under the same visible-light conditions. After the combination of calibration process, the average gaze tracking errors by the proposed YOLOv3-tiny-based pupil tracking models are smaller than 2.9 and 3.5 degrees at the training and testing modes, respectively, and the proposed visible-light wearable gaze tracking system performs up to 20 frames per second (FPS) on the GPU-based software embedded platform.

scholarly journals Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion

2020 ◽  
Vol 12 (20) ◽  
pp. 3316 ◽  
Author(s):  
Yulian Zhang ◽  
Lihong Guo ◽  
Zengfa Wang ◽  
Yang Yu ◽  
Xinwei Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Atmospheric Correction ◽  
Recall Rate ◽  
Google Earth ◽  
Superior Performance ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Ship Detection

Intelligent detection and recognition of ships from high-resolution remote sensing images is an extraordinarily useful task in civil and military reconnaissance. It is difficult to detect ships with high precision because various disturbances are present in the sea such as clouds, mist, islands, coastlines, ripples, and so on. To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion (CFF-SDN). Our ship detection network consists of three parts. Firstly, the convolutional feature extraction network is used to extract ship features of different levels. Residual connection is introduced so that the model can be designed very deeply, and it is easy to train and converge. Secondly, the proposed network fuses fine-grained features from shallow layers with semantic features from deep layers, which is beneficial for detecting ship targets with different sizes. At the same time, it is helpful to improve the localization accuracy and detection accuracy of small objects. Finally, multiple fused feature maps are used for classification and regression, which can adapt to ships of multiple scales. Since the CFF-SDN model uses a pruning strategy, the detection speed is greatly improved. In the experiment, we create a dataset for ship detection in remote sensing images (DSDR), including actual satellite images from Google Earth and aerial images from electro-optical pod. The DSDR dataset contains not only visible light images, but also infrared images. To improve the robustness to various sea scenes, images under different scales, perspectives and illumination are obtained through data augmentation or affine transformation methods. To reduce the influence of atmospheric absorption and scattering, a dark channel prior is adopted to solve atmospheric correction on the sea scenes. Moreover, soft non-maximum suppression (NMS) is introduced to increase the recall rate for densely arranged ships. In addition, better detection performance is observed in comparison with the existing models in terms of precision rate and recall rate. The experimental results show that the proposed detection model can achieve the superior performance of ship detection in optical remote sensing image.

scholarly journals Webshell Detection Based on Executable Data Characteristics of PHP Code

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zulie Pan ◽  
Yuanchao Chen ◽  
Yu Chen ◽  
Yi Shen ◽  
Xuanzhen Guo
Keyword(s):  
State Of The Art ◽  
Recall Rate ◽  
Remote Access ◽  
Detection Accuracy ◽  
Controlled Experiments ◽  
Data Set ◽  
Detection Model ◽  
Proposed Model ◽  
Text Features ◽  
And Control

A webshell is a malicious backdoor that allows remote access and control to a web server by executing arbitrary commands. The wide use of obfuscation and encryption technologies has greatly increased the difficulty of webshell detection. To this end, we propose a novel webshell detection model leveraging the grammatical features extracted from the PHP code. The key idea is to combine the executable data characteristics of the PHP code with static text features for webshell classification. To verify the proposed model, we construct a cleaned data set of webshell consisting of 2,917 samples from 17 webshell collection projects and conduct extensive experiments. We have designed three sets of controlled experiments, the results of which show that the accuracy of the three algorithms has reached more than 99.40%, the highest reached 99.66%, the recall rate has been increased by at least 1.8%, the most increased by 6.75%, and the F1 value has increased by 2.02% on average. It not only confirms the efficiency of the grammatical features in webshell detection but also shows that our system significantly outperforms several state-of-the-art rivals in terms of detection accuracy and recall rate.

Armored Target Detection in Battlefield Environment Based on Top-Down Aggregation Network and Hierarchical Scale Optimization

2019 ◽  
Vol 33 (04) ◽  
pp. 1950007 ◽  
Author(s):  
Haoze Sun ◽  
Tianqing Chang ◽  
Lei Zhang ◽  
Guozhen Yang ◽  
Bin Han ◽  
...  
Keyword(s):  
Target Detection ◽  
Region Of Interest ◽  
Recall Rate ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Top Down ◽  
Long Distance ◽  
General Object ◽  
Battlefield Environment

Armored equipment plays a crucial role in the ground battlefield. The fast and accurate detection of enemy armored targets is significant to take the initiative in the battlefield. Comparing to general object detection and vehicle detection, armored target detection in battlefield environment is more challenging due to the long distance of observation and the complicated environment. In this paper, an accurate and robust automatic detection method is proposed to detect armored targets in battlefield environment. Firstly, inspired by Feature Pyramid Network (FPN), we propose a top-down aggregation (TDA) network which enhances shallow feature maps by aggregating semantic information from deeper layers. Then, using the proposed TDA network in a basic Faster R-CNN framework, we explore the further optimization of the approach for armored target detection: for the Region of Interest (RoI) Proposal Network (RPN), we propose a multi-branch RPNs framework to generate proposals that match the scale of armored targets and the specific receptive field of each aggregated layer and design hierarchical loss for the multi-branch RPNs; for RoI Classifier Network (RCN), we apply RoI pooling on the single finest scale feature map and construct a light and fast detection network. To evaluate our method, comparable experiments with state-of-art detection methods were conducted on a challenging dataset of images with armored targets. The experimental results demonstrate the effectiveness of the proposed method in terms of detection accuracy and recall rate.

scholarly journals SIFT Feature-Based Video Camera Boundary Detection Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lingqiang Kong
Keyword(s):  
Complex Structure ◽  
Boundary Detection ◽  
Recall Rate ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Gradual Change ◽  
Dynamic Texture ◽  
Sift Feature ◽  
Film And Television ◽  
Feature Based

Aiming at the problem of low accuracy of edge detection of the film and television lens, a new SIFT feature-based camera detection algorithm was proposed. Firstly, multiple frames of images are read in time sequence and converted into grayscale images. The frame image is further divided into blocks, and the average gradient of each block is calculated to construct the film dynamic texture. The correlation of the dynamic texture of adjacent frames and the matching degree of SIFT features of two frames were compared, and the predetection results were obtained according to the matching results. Next, compared with the next frame of the dynamic texture and SIFT feature whose step size is lower than the human eye refresh frequency, the final result is obtained. Through experiments on multiple groups of different types of film and television data, high recall rate and accuracy rate can be obtained. The algorithm in this paper can detect the gradual change lens with the complex structure and obtain high detection accuracy and recall rate. A lens boundary detection algorithm based on fuzzy clustering is realized. The algorithm can detect sudden changes/gradual changes of the lens at the same time without setting a threshold. It can effectively reduce the factors that affect lens detection, such as flash, movies, TV, and advertisements, and can reduce the influence of camera movement on the boundaries of movies and TVs. However, due to the complexity of film and television, there are still some missing and false detections in this algorithm, which need further study.

Intelligent system for predicting breast tumors using machine learning

2020 ◽  
Vol 39 (4) ◽  
pp. 4813-4822
Author(s):  
Meifang Li ◽  
Binlin Ruan ◽  
Caixing Yuan ◽  
Zhishuang Song ◽  
Chongchong Dai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Breast Tumor ◽  
Intelligent System ◽  
Breast Tumors ◽  
Detection Accuracy ◽  
Segmentation Method ◽  
X Ray ◽  
Image Diagnosis ◽  
Mammogram Image ◽  
Aided Diagnosis

The early hidden characteristics of breast tumors make their features difficult to be effectively identified. In order to improve the detection accuracy of breast tumors, this study combined with computer-aided diagnosis techniques such as machine learning and computer vision and used X-ray analysis to study breast tumor diagnosis techniques. Moreover, this study combines breast tumor diagnostic images to determine various parameters of the image. At the same time, through experimental research and analysis of the region segmentation method and preprocessing method of breast detection images, the best diagnostic images are obtained, and the influence of background and other noise on the image diagnosis results is effectively proposed. In addition, this study proposes a method for detecting the distortion of the mammogram image structure, which accurately detects the structural distortion and reduces the interference of various influencing factors. Finally, this paper designs experiments to study the effects of the diagnostic method of this paper. Through comparative analysis, it can be seen that the results of this study have certain advantages in accuracy and image clarity, and have certain clinical significance, and can provide theoretical reference for subsequent related research.

scholarly journals Real-Time Segmentation Method of Lightweight Network For Finger Vein Using Embedded Terminal Technique

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 303-316
Author(s):  
Junying Zeng ◽  
Boyuan Zhu ◽  
Yujie Huang ◽  
Chuanbo Qin ◽  
Jingming Zhu ◽  
...  
Keyword(s):  
Real Time ◽  
Segmentation Method ◽  
Finger Vein ◽  
Embedded Terminal ◽  
Time Segmentation

scholarly journals A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid-Coded HLO

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Pinggai Zhang ◽  
Minrui Fei ◽  
Ling Wang ◽  
Xian Wu ◽  
Chen Peng ◽  
...  
Keyword(s):  
State Of The Art ◽  
Color Model ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Optimal Parameters ◽  
Segmentation Method ◽  
Safe Operation ◽  
False Detection ◽  
High Detection ◽  
Optimal Set

In recent years, the combustion furnace has been widely applied in many different fields of industrial technology, and the accurate detection of combustion states can effectively help operators adjust combustion strategies to improve combustion utilization and ensure safe operation. However, the combustion states inside the industrial furnace change according to the production needs, which further challenges the optimal set of model parameters. To effectively segment the flame pixels, a novel segmentation method for furnace flame using adaptive color model and hybrid-coded human learning optimization (AHcHLO) is proposed. A new adaptive color model with mixed variables (NACMM) is designed for adapting to different combustion states, and the AHcHLO is developed to search for the optimal parameters of NACMM. Then, the best NACMM with optimal parameters is adopted to segment the combustion flame image more precisely and effectively. Finally, the experiment results show that the developed AHcHLO obtains the best-known overall results so far on benchmark functions and the proposed NACMM outperforms state-of-the-art flame segmentation approaches, providing a high detection accuracy and a low false detection rate.

Performance Evaluation of Edge Orientation Histograms Based System for Real-time Object Detection in Two Separate Platforms

2020 ◽  
Vol 13 (1) ◽  
pp. 86-90 ◽  
Author(s):  
Souhail Guennouni ◽  
Anass Mansouri ◽  
Ali Ahaitouf
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Detection System ◽  
Image Database ◽  
Local Binary Patterns ◽  
Detection Accuracy ◽  
Edge Orientation ◽  
Embedded Platform ◽  
Main Challenge ◽  
Reliable Detection

Background: Real-time object detection has been attracting much attention recently due the increasing market need of such systems. Therefore, different detection algorithms and techniques have been evaluated to create a reliable detection system. The main challenge to implement a realtime reliable detection system relies on the algorithm training phase. During this phase, a large number of object image database needs to be prepared for each object to be detected. Objective: In this work, we implement a simultaneous object detection system based on local Edge Orientation Histograms (EOH) as feature extraction method with a smaller objects image database. Then, we evaluate the performance of this detection system in two separate platforms. Methods: We evaluated the performance of the detection of Ede Orientation Histograms against HAAR and Local Binary Patterns (LBP) algorithms using two different objects. After that, we discussed the evaluation of the detection systems on the standard platform in addition to the porting process into the embedded platform. Results: We achieved excellent results on both face and hands objects using less than 300 samples. This number is really negligible compared to the size of the image database used by state-of-the-art solutions. In terms of quality of detection, we have achieved more than 93% detection accuracy for the standard platform and 91.8% in the embedded platform for both face and hand objects. Conclusion: In this work, we demonstrated how Edge Orientation Histograms-based detection system gives better performance results than the algorithms evaluated against with less than 300 images database in two separate platforms.

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