scholarly journals Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions

2021 ◽  
Vol 14 (1) ◽  
pp. 106
Author(s):  
Cheng Chen ◽  
Sindhu Chandra ◽  
Yufan Han ◽  
Hyungjoon Seo
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Defect Detection ◽  
Input Data ◽  
Thermal Image ◽  
Detection Accuracy ◽  
Detection Model ◽  
Fused Image ◽  
Data Source ◽  
Pavement Damage

Automatic damage detection using deep learning warrants an extensive data source that captures complex pavement conditions. This paper proposes a thermal-RGB fusion image-based pavement damage detection model, wherein the fused RGB-thermal image is formed through multi-source sensor information to achieve fast and accurate defect detection including complex pavement conditions. The proposed method uses pre-trained EfficientNet B4 as the backbone architecture and generates an argument dataset (containing non-uniform illumination, camera noise, and scales of thermal images too) to achieve high pavement damage detection accuracy. This paper tests separately the performance of different input data (RGB, thermal, MSX, and fused image) to test the influence of input data and network on the detection results. The results proved that the fused image’s damage detection accuracy can be as high as 98.34% and by using the dataset after augmentation, the detection model deems to be more stable to achieve 98.35% precision, 98.34% recall, and 98.34% F1-score.

scholarly journals BUILDING CHANGE DETECTION FROM BITEMPORAL AERIAL IMAGES USING DEEP LEARNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 565-571
Author(s):  
S. Su ◽  
T. Nawata ◽  
T. Fuse
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Urban Areas ◽  
Network Architecture ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Detection Model ◽  
Wide Range ◽  
Surface Models ◽  
Data Source

Abstract. Automatic building change detection has become a topical issue owing to its wide range of applications, such as updating building maps. However, accurate building change detection remains challenging, particularly in urban areas. Thus far, there has been limited research on the use of the outdated building map (the building map before the update, referred to herein as the old-map) to increase the accuracy of building change detection. This paper presents a novel deep-learning-based method for building change detection using bitemporal aerial images containing RGB bands, bitemporal digital surface models (DSMs), and an old-map. The aerial images have two types of spatial resolutions, 12.5 cm or 16 cm, and the cell size of the DSMs is 50 cm × 50 cm. The bitemporal aerial images, the height variations calculated using the differences between the bitemporal DSMs, and the old-map were fed into a network architecture to build an automatic building change detection model. The performance of the model was quantitatively and qualitatively evaluated for an urban area that covered approximately 10 km2 and contained over 21,000 buildings. The results indicate that it can detect the building changes with optimum accuracy as compared to other methods that use inputs such as i) bitemporal aerial images only, ii) bitemporal aerial images and bitemporal DSMs, and iii) bitemporal aerial images and an old-map. The proposed method achieved recall rates of 89.3%, 88.8%, and 99.5% for new, demolished, and other buildings, respectively. The results also demonstrate that the old-map is an effective data source for increasing building change detection accuracy.

scholarly journals Deep Learning Model for the Inspection of Coffee Bean Defects

2021 ◽  
Vol 11 (17) ◽  
pp. 8226
Author(s):  
Shyang-Jye Chang ◽  
Chien-Yu Huang
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Learning Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Coffee Bean ◽  
Accuracy Rate ◽  
Detection Model ◽  
Proposed Model ◽  
Coffee Beans

The detection of coffee bean defects is the most crucial step prior to bean roasting. Existing defect detection methods used in the specialty coffee bean industry entail manual screening and sorting, require substantial human resources, and are not standardized. To solve these problems, this study developed a deep learning algorithm to detect defects in coffee beans. The results reveal that when the pooling layer was used to enhance features and reduce neural dimensionality, some of the coffee been features were lost or misclassified. Therefore, a novel dimensionality reduction method was adopted to increase the ability of feature extraction. The developed model also overcame the drawbacks of padding causing blurred image boundaries and the dead neurons causing impeding feature propagation. Images of eight types of coffee beans were used to train and test the proposed detection model. The proposed method was verified to reduce the bias when classifying defects in coffee beans. The detection accuracy rate of the proposed model was 95.2%. When the model was only used to detect the presence of defects, the accuracy rate increased to 100%. Thus, the proposed model is highly accurate in coffee bean defect detection in the classification of eight types of coffee beans.

scholarly journals A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks

2020 ◽  
Vol 17 (22) ◽  
pp. 8447
Author(s):  
Dima M. Alalharith ◽  
Hajar M. Alharthi ◽  
Wejdan M. Alghamdi ◽  
Yasmine M. Alsenbel ◽  
Nida Aslam ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Periodontal Disease ◽  
Convolutional Neural Network ◽  
Region Of Interest ◽  
Training Dataset ◽  
Detection Accuracy ◽  
Detection Model ◽  
Art Object ◽  
Orthodontic Patients

Computer-based technologies play a central role in the dentistry field, as they present many methods for diagnosing and detecting various diseases, such as periodontitis. The current study aimed to develop and evaluate the state-of-the-art object detection and recognition techniques and deep learning algorithms for the automatic detection of periodontal disease in orthodontic patients using intraoral images. In this study, a total of 134 intraoral images were divided into a training dataset (n = 107 [80%]) and a test dataset (n = 27 [20%]). Two Faster Region-based Convolutional Neural Network (R-CNN) models using ResNet-50 Convolutional Neural Network (CNN) were developed. The first model detects the teeth to locate the region of interest (ROI), while the second model detects gingival inflammation. The detection accuracy, precision, recall, and mean average precision (mAP) were calculated to verify the significance of the proposed model. The teeth detection model achieved an accuracy, precision, recall, and mAP of 100 %, 100%, 51.85%, and 100%, respectively. The inflammation detection model achieved an accuracy, precision, recall, and mAP of 77.12%, 88.02%, 41.75%, and 68.19%, respectively. This study proved the viability of deep learning models for the detection and diagnosis of gingivitis in intraoral images. Hence, this highlights its potential usability in the field of dentistry and aiding in reducing the severity of periodontal disease globally through preemptive non-invasive diagnosis.

scholarly journals Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yiran Feng ◽  
Xueheng Tao ◽  
Eung-Joo Lee
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Multiple Objects ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Quality Sorting

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

scholarly journals A New Steel Defect Detection Algorithm Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weidong Zhao ◽  
Feng Chen ◽  
Hancheng Huang ◽  
Dan Li ◽  
Wei Cheng
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Defect Detection ◽  
Steel Surface ◽  
Surface Defect ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Average Precision ◽  
Original Algorithm ◽  
Surface Defect Detection

In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a problem to be solved. The authors of this article have understood the shortcomings of the deep learning detection algorithm in detecting small and complex defect targets and would like to share a new improved target detection algorithm in steel surface defect detection. The steel surface defects will affect the quality of steel seriously. We find that most of the current detection algorithms for NEU-DET dataset detection accuracy are low, so we choose to verify a steel surface defect detection algorithm based on machine vision on this dataset for the problem of defect detection in steel production. A series of improvement measures are carried out in the traditional Faster R-CNN algorithm, such as reconstructing the network structure of Faster R-CNN. Based on the small features of the target, we train the network with multiscale fusion. For the complex features of the target, we replace part of the conventional convolution network with a deformable convolution network. The experimental results show that the deep learning network model trained by the proposed method has good detection performance, and the mean average precision is 0.752, which is 0.128 higher than the original algorithm. Among them, the average precision of crazing, inclusion, patches, pitted surface, rolled in scale and scratches is 0.501, 0.791, 0.792, 0.874, 0.649, and 0.905, respectively. The detection method is able to identify small target defects on the steel surface effectively, which can provide a reference for the automatic detection of steel defects.

scholarly journals BLNN: Multiscale Feature Fusion-Based Bilinear Fine-Grained Convolutional Neural Network for Image Classification of Wood Knot Defects

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingyu Gao ◽  
Fei Wang ◽  
Peng Song ◽  
Junyan Liu ◽  
DaWei Qi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Defect Detection ◽  
Feature Fusion ◽  
Image Features ◽  
Optical Image ◽  
Training Time ◽  
Fine Grained ◽  
Detection Model ◽  
Defect Recognition

Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves the wood utilization. The traditional neural network technique is unemployed for the wood defect detection of optical image used, which results from a long training time, low recognition accuracy, and nonautomatic extraction of defect image features. In this paper, a wood knot defect detection model (so-called BLNN) combined deep learning is reported. Two subnetworks composed of convolutional neural networks are trained by Pytorch. By using the feature extraction capabilities of the two subnetworks and combining the bilinear join operation, the fine-grained features of the image are obtained. The experimental results show that the accuracy has reached up 99.20%, and the training time is obviously reduced with the speed of defect detection about 0.0795 s/image. It indicates that BLNN has the ability to improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.

scholarly journals A METHOD OF BUILDING DETECTION IN REMOTE SENSING IMAGES BASED ON DEEP LEARNING WITH MULTIPLE LIGHTNESS DETECTORS

2019 ◽  
Vol XLII-4/W20 ◽  
pp. 27-31
Author(s):  
D. M. Huang ◽  
H. R. Zhao ◽  
Y. Yao
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Sustainable Development ◽  
Deep Learning ◽  
Detection Accuracy ◽  
Building Detection ◽  
Remote Sensing Images ◽  
Building Conditions ◽  
Building Information ◽  
Data Source

Abstract. Buildings, where most human activities happen, are one of the most important crucial objects in remote sensing images. Extracting building information is of great significance importance for conducting sustainable development-related researches. The extracted building information is a fundamental data source for further researches, including evaluating the living conditions of people, monitoring building conditions, predicting disaster risks and so on. In recent years, convolutional neural networks have been widely employed in building detection, and have gained significant progresses. However, in these automatic detection procedures, the critical brightness information is often neglected, with all buildings simply classified into the same category. To make the building detection more efficient and precise, we propose a simple yet efficient multitask method employing several lightness detectors, each of which is dedicated to the building detection in a specific brightness interval. Experiment results show that the building detection accuracy could be improved by 8.1% with the assistance of the additional lightness information.

Facemask Detection Based on Double Convolutional Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Guoqiang Chen ◽  
Bingxin Bai ◽  
Hongpeng Zhou ◽  
Mengchao Liu ◽  
Huailong Yi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Contextual Information ◽  
Detection Accuracy ◽  
Learning Networks ◽  
Position Information ◽  
Detection Model ◽  
Multi Stage

Background: The study on facemask detection is of great significance because facemask detection is difficult, and the workload is heavy in places with a large number of people during the COVID-19 outbreak. Objective: The study aims to explore new deep learning networks that can accurately detect facemasks and improve the network's ability to extract multi-level features and contextual information. In addition, the proposed network effectively avoids the interference of objects like masks. The new network could eventually detect masks wearers in the crowd. Method: A Multi-stage Feature Fusion Block (MFFB) and a Detector Cascade Block (DCB) are proposed and connected to the deep learning network for facemask detection. The network's ability to obtain information improves. The network proposed in the study is Double Convolutional Neural Networks (CNN) called DCNN, which can fuse mask features and face position information. During facemask detection, the network extracts the featural information of the object and then inputs it into the data fusion layer. Results: The experiment results show that the proposed network can detect masks and faces in a complex environment and dense crowd. The detection accuracy of the network improves effectively. At the same time, the real-time performance of the detection model is excellent. Conclusion: The two branch networks of the DCNN can effectively obtain the feature and position information of facemasks. The network overcomes the disadvantage that a single CNN is susceptible to the interference of the suspected mask objects. The verification shows that the MFFB and the DCB can improve the network's ability to obtain object information, and the proposed DCNN can achieve excellent detection performance.

scholarly journals Detection Model of Occluded Object Based on YOLO Using Hard-Example Mining and Augmentation Policy Optimization

2021 ◽  
Vol 11 (15) ◽  
pp. 7093
Author(s):  
Seong-Eun Ryu ◽  
Kyung-Yong Chung
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Fine Tuning ◽  
Detection Accuracy ◽  
Model Learning ◽  
Improvement Rate ◽  
Detection Model ◽  
Occluded Objects ◽  
Object Based ◽  
Policy Optimization

A study on object detection utilizing deep learning is in continuous progress to promptly and accurately determine the surrounding situation in the driving environment. Existing studies have tried to improve object detection performance considering occlusion through various processes. However, recent studies use R-CNN-based deep learning to provide high accuracy at slow speeds, so there are limitations to real-time. In addition, since such previous studies never took into consideration the data imbalance problem of the objects of interest in the model training process, it is necessary to make additional improvements. Accordingly, we proposed a detection model of occluded object based on YOLO using hard-example mining and augmentation policy optimization. The proposed procedures were as follows: diverse augmentation policies were applied to the base model in sequence and the optimized policy suitable for training data were strategically selected through the gradient-based performance improvement rate. Then, in the model learning process, the occluded objects and the objects likely to induce a false-positive detection were extracted, and fine-tuning using transfer learning was conducted. As a result of the performance evaluation, the model proposed in this study showed an [email protected] value of 90.49% and an F1-score value of 90%. It showed that this model detected occluded objects more stably and significantly enhanced the self-driving object detection accuracy compared with existing model.

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