scholarly journals Detection of Micro-Defects on Irregular Reflective Surfaces Based on Improved Faster R-CNN

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5000 ◽  
Author(s):  
Zhuangzhuang Zhou ◽  
Qinghua Lu ◽  
Zhifeng Wang ◽  
Haojie Huang
Keyword(s):  
Specular Reflection ◽  
Receptive Fields ◽  
Ground Truth ◽  
Detection Performance ◽  
Support Vector ◽  
Detection Accuracy ◽  
Irregular Surfaces ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Improved Model

The detection of defects on irregular surfaces with specular reflection characteristics is an important part of the production process of sanitary equipment. Currently, defect detection algorithms for most irregular surfaces rely on the handcrafted extraction of shallow features, and the ability to recognize these defects is limited. To improve the detection accuracy of micro-defects on irregular surfaces in an industrial environment, we propose an improved Faster R-CNN model. Considering the variety of defect shapes and sizes, we selected the K-Means algorithm to generate the aspect ratio of the anchor box according to the size of the ground truth, and the feature matrices are fused with different receptive fields to improve the detection performance of the model. The experimental results show that the recognition accuracy of the improved model is 94.6% on a collected ceramic dataset. Compared with SVM (Support Vector Machine) and other deep learning-based models, the proposed model has better detection performance and robustness to illumination, which proves the practicability and effectiveness of the proposed method.

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

2021 ◽  
Vol 15 (4) ◽  
pp. 18-30
Author(s):  
Om Prakash Samantray ◽  
Satya Narayan Tripathy
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

scholarly journals Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3415 ◽  
Author(s):  
Jinpeng Zhang ◽  
Jinming Zhang ◽  
Shan Yu
Keyword(s):  
Object Detection ◽  
Sampling Method ◽  
Ground Truth ◽  
Detection Accuracy ◽  
Two Stage ◽  
Baseline Model ◽  
Detection Algorithms ◽  
Imbalance Problem ◽  
Image Object Detection ◽  
Image Object

In the image object detection task, a huge number of candidate boxes are generated to match with a relatively very small amount of ground-truth boxes, and through this method the learning samples can be created. But in fact the vast majority of the candidate boxes do not contain valid object instances and should be recognized and rejected during the training and evaluation of the network. This leads to extra high computation burden and a serious imbalance problem between object and none-object samples, thereby impeding the algorithm’s performance. Here we propose a new heuristic sampling method to generate candidate boxes for two-stage detection algorithms. It is generally applicable to the current two-stage detection algorithms to improve their detection performance. Experiments on COCO dataset showed that, relative to the baseline model, this new method could significantly increase the detection accuracy and efficiency.

scholarly journals Combustible Gas Classification Modeling using Support Vector Machine and Pairing Plot Scheme

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5018 ◽  
Author(s):  
Kyu-Won Jang ◽  
Jong-Hyeok Choi ◽  
Ji-Hoon Jeon ◽  
Hyun-Seok Kim
Keyword(s):  
Support Vector Machine ◽  
Gas Sensors ◽  
Learning Algorithm ◽  
Support Vector ◽  
Detection Accuracy ◽  
Minimum Concentration ◽  
Industrial Sites ◽  
Combustible Gas ◽  
Proposed Model ◽  
Combustible Gases

Combustible gases, such as CH4 and CO, directly or indirectly affect the human body. Thus, leakage detection of combustible gases is essential for various industrial sites and daily life. Many types of gas sensors are used to identify these combustible gases, but since gas sensors generally have low selectivity among gases, coupling issues often arise which adversely affect gas detection accuracy. To solve this problem, we built a decoupling algorithm with different gas sensors using a machine learning algorithm. Commercially available semiconductor sensors were employed to detect CH4 and CO, and then support vector machine (SVM) applied as a supervised learning algorithm for gas classification. We also introduced a pairing plot scheme to more effectively classify gas type. The proposed model classified CH4 and CO gases 100% correctly at all levels above the minimum concentration the gas sensors could detect. Consequently, SVM with pairing plot is a memory efficient and promising method for more accurate gas classification.

Structured Pyramidal Neural Networks

2018 ◽  
Vol 28 (05) ◽  
pp. 1750021 ◽  
Author(s):  
Alessandra M. Soares ◽  
Bruno J. T. Fernandes ◽  
Carmelo J. A. Bastos-Filho
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Processing Time ◽  
Receptive Fields ◽  
Memory Capacity ◽  
The Other ◽  
Support Vector ◽  
Proposed Model ◽  
Novel Method ◽  
Original Approach

The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.

A Novel Hybrid LE and SVM with CV in Intrusion Detection

2014 ◽  
Vol 644-650 ◽  
pp. 2572-2576
Author(s):  
Qing Liu ◽  
Yun Kai Zhang ◽  
Qing Ru Li
Keyword(s):  
Intrusion Detection ◽  
False Positive Rate ◽  
Likelihood Estimation ◽  
Support Vector ◽  
Training Time ◽  
Detection Algorithms ◽  
Lower False Positive Rate ◽  
Proposed Model ◽  
Positive Rate ◽  
Rbf Kernel

A support vector machine (SVM) model combined Laplacian Eigenmaps (LE) with Cross Validation (CV) is proposed for intrusion detection. In the proposed model, a classifier is adopted to estimate whether an action is an attack or not. Maximum Likelihood Estimation (MLE) is used to estimate the intrinsic dimensions, and LE is used as a preprocessor of SVM to reduce the dimensions of feature vectors then training time is shortened. In order to improve the performance of SVM, CV is used to optimize the parameters of SVM in RBF kernel function. Compared with other detection algorithms, the experimental results show that the proposed model has the advantages: shorter training time, higher accuracy rate and lower false positive rate.

scholarly journals YOLOv3-Litchi Detection Method of Densely Distributed Litchi in Large Vision Scenes

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongjun Wang ◽  
Lizhong Dong ◽  
Hao Zhou ◽  
Lufeng Luo ◽  
Guichao Lin ◽  
...  
Keyword(s):  
Detection Performance ◽  
Detection Methods ◽  
Natural Environments ◽  
Test Results ◽  
Detection Time ◽  
Proposed Model ◽  
Bounding Boxes ◽  
Improved Model ◽  
Detection Speed ◽  
Detection Effect

Accurate and reliable fruit detection in the orchard environment is an important step for yield estimation and robotic harvesting. However, the existing detection methods often target large and relatively sparse fruits, but they cannot provide a good solution for small and densely distributed fruits. This paper proposes a YOLOv3-Litchi model based on YOLOv3 to detect densely distributed litchi fruits in large visual scenes. We adjusted the prediction scale and reduced the network layer to improve the detection ability of small and dense litchi fruits and ensure the detection speed. From flowering to 50 days after maturity, we collected a total of 266 images, including 16,000 fruits, and then used them to construct the litchi dataset. Then, the k-means++ algorithm is used to cluster the bounding boxes in the labeled data to determine the priori box size suitable for litchi detection. We trained an improved YOLOv3-Litchi model, tested its litchi detection performance, and compared YOLOv3-Litchi with YOLOv2, YOLOv3, and Faster R-CNN on the actual detection effect of litchi and used the F1 value and the average detection time as the assessed value. The test results show that the F1 of YOLOv3-Litchi is higher than that of YOLOv2 algorithm 0.1, higher than that of YOLOv3 algorithm 0.08, and higher than that of Faster R-CNN algorithm 0.05; the average detection time of YOLOv3-Litchi is 29.44 ms faster than that of YOLOv2 algorithm, 19.56 ms faster than that of YOLOv3 algorithm ms, and 607.06 ms faster than that of Faster R-CNN algorithm. And the detection speed of the improved model is faster. The proposed model remits optimal detection performance for small and dense fruits. The work presented here may provide a reference for further study on fruit-detection methods in natural environments.

scholarly journals IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 286 ◽  
Author(s):  
Jiangqiao Yan ◽  
Hongqi Wang ◽  
Menglong Yan ◽  
Wenhui Diao ◽  
Xian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Ground Truth ◽  
Detection Performance ◽  
Candidate Region ◽  
Detection Accuracy ◽  
Remote Sensing Images

Recently, methods based on Faster region-based convolutional neural network (R-CNN)have been popular in multi-class object detection in remote sensing images due to their outstandingdetection performance. The methods generally propose candidate region of interests (ROIs) througha region propose network (RPN), and the regions with high enough intersection-over-union (IoU)values against ground truth are treated as positive samples for training. In this paper, we find thatthe detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially,detection performance of small objects is poor when choosing a normal higher threshold, while alower threshold will result in poor location accuracy caused by a large quantity of false positives.To address the above issues, we propose a novel IoU-Adaptive Deformable R-CNN framework formulti-class object detection. Specially, by analyzing the different roles that IoU can play in differentparts of the network, we propose an IoU-guided detection framework to reduce the loss of small objectinformation during training. Besides, the IoU-based weighted loss is designed, which can learn theIoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspectratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves theprecision of the results. Extensive experiments validate the effectiveness of our approach and weachieve state-of-the-art detection performance on the DOTA dataset.

scholarly journals Deep Learning Approach for Building Detection Using LiDAR–Orthophoto Fusion

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Faten Hamed Nahhas ◽  
Helmi Z. M. Shafri ◽  
Maher Ibrahim Sameen ◽  
Biswajeet Pradhan ◽  
Shattri Mansor
Keyword(s):  
Deep Learning ◽  
Dimensionality Reduction ◽  
Batch Size ◽  
Support Vector ◽  
Detection Accuracy ◽  
Building Detection ◽  
Testing Area ◽  
Building Recognition ◽  
Proposed Model ◽  
High Level

This paper reports on a building detection approach based on deep learning (DL) using the fusion of Light Detection and Ranging (LiDAR) data and orthophotos. The proposed method utilized object-based analysis to create objects, a feature-level fusion, an autoencoder-based dimensionality reduction to transform low-level features into compressed features, and a convolutional neural network (CNN) to transform compressed features into high-level features, which were used to classify objects into buildings and background. The proposed architecture was optimized for the grid search method, and its sensitivity to hyperparameters was analyzed and discussed. The proposed model was evaluated on two datasets selected from an urban area with different building types. Results show that the dimensionality reduction by the autoencoder approach from 21 features to 10 features can improve detection accuracy from 86.06% to 86.19% in the working area and from 77.92% to 78.26% in the testing area. The sensitivity analysis also shows that the selection of the hyperparameter values of the model significantly affects detection accuracy. The best hyperparameters of the model are 128 filters in the CNN model, the Adamax optimizer, 10 units in the fully connected layer of the CNN model, a batch size of 8, and a dropout of 0.2. These hyperparameters are critical to improving the generalization capacity of the model. Furthermore, comparison experiments with the support vector machine (SVM) show that the proposed model with or without dimensionality reduction outperforms the SVM models in the working area. However, the SVM model achieves better accuracy in the testing area than the proposed model without dimensionality reduction. This study generally shows that the use of an autoencoder in DL models can improve the accuracy of building recognition in fused LiDAR–orthophoto data.

scholarly journals Improved Detection Criteria for Detecting Drug-Drug Interaction Signals Using the Proportional Reporting Ratio

2020 ◽  
Vol 14 (1) ◽  
pp. 4
Author(s):  
Yoshihiro Noguchi ◽  
Keisuke Aoyama ◽  
Satoaki Kubo ◽  
Tomoya Tachi ◽  
Hitomi Teramachi
Keyword(s):  
Drug Interaction ◽  
Confidence Interval ◽  
Drug Interactions ◽  
Signal Detection ◽  
Proportional Reporting Ratio ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Signal Score ◽  
Drug Drug Interaction

There is a current demand for “safety signal” screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. “Hypothetical” true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden’s index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.

scholarly journals Detecting Multielement Algorithmically Generated Domain Names Based on Adaptive Embedding Model

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Luhui Yang ◽  
Guangjie Liu ◽  
Weiwei Liu ◽  
Huiwen Bai ◽  
Jiangtao Zhai ◽  
...  
Keyword(s):  
Detection Accuracy ◽  
Domain Name ◽  
Domain Names ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Adaptive Embedding ◽  
Series Of Experiments ◽  
Dynamic Loss ◽  
Comparison Algorithms ◽  
Dynamic Domain

With the development of detection algorithms on malicious dynamic domain names, domain generation algorithms have developed to be more stealthy. The use of multiple elements for generating domains will lead to higher detection difficulty. To effectively improve the detection accuracy of algorithmically generated domain names based on multiple elements, a domain name syntax model is proposed, which analyzes the multiple elements in domain names and their syntactic relationship, and an adaptive embedding method is proposed to achieve effective element parsing of domain names. A parallel convolutional model based on the feature selection module combined with an improved dynamic loss function based on curriculum learning is proposed, which can achieve effective detection on multielement malicious domain names. A series of experiments are designed and the proposed model is compared with five previous algorithms. The experimental results denote that the detection accuracy of the proposed model for multiple-element malicious domain names is significantly higher than that of the comparison algorithms and also has good adaptability to other types of malicious domain names.

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