A Lightweight Spatial and Temporal Multi-Feature Fusion Network for Defect Detection

Hu, Bozhen; Gao, Bin; Woo, Wai Lok; Ruan, Lingfeng; Jin, Juan; Yang, Yang; Yu, Yongjie

doi:10.1109/tip.2020.3036770

Cited by 64 publications

(17 citation statements)

References 31 publications

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“…Experiments were established to identify the optimal batch size, epochs, and the number of nodes for individual machine learning model i.e., LR, ANN with a single hidden layer (ANN1), ANN with 2 hidden layers (ANN2), ANN with 3 hidden layers (ANN3), GRU, LSTM, Bi-LSTM. The optimal parameterization was conducted according to the following sets [ 55 ] i.e., batch size: [8, 16, 32, 64, 128], epochs: [10, 20, 30, 40, 50], and each hidden layer the number of neurons were set as [8, 16, 32, 64, 128, 256, 512]. For all the experiments the parameters of the ANN were configured as the following: an initializing function is a uniform function; activation functions used the ReLU function in hidden layer and the sigmoid function for the output layer.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

Tengtrairat

Woo

Parathai

et al. 2021

Sensors

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Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations—Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

Tengtrairat

Woo

Parathai

et al. 2021

Sensors

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“…Sun et al [24] proposed an adaptive saliency-biased loss (ASBL) to train the RetinaNet and dramatically improved the performance of detection in the ORSIs. In addition, the work in [25,26] proposed the advanced object detection architecture that involves both spatial and temporal domain information in the decision. However, these axis-aligned bounding box object detectors are still confronted with the challenge of arbitrary orientations in ORSIs.…”

Section: One-stage Object Detection Methodsmentioning

confidence: 99%

Multi-Sector Oriented Object Detector for Accurate Localization in Optical Remote Sensing Images

et al. 2021

Remote Sensing

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Oriented object detection in optical remote sensing images (ORSIs) is a challenging task since the targets in ORSIs are displayed in an arbitrarily oriented manner and on small scales, and are densely packed. Current state-of-the-art oriented object detection models used in ORSIs primarily evolved from anchor-based and direct regression-based detection paradigms. Nevertheless, they still encounter a design difficulty from handcrafted anchor definitions and learning complexities in direct localization regression. To tackle these issues, in this paper, we proposed a novel multi-sector oriented object detection framework called MSO2-Det, which quantizes the scales and orientation prediction of targets in ORSIs via an anchor-free classification-to-regression approach. Specifically, we first represented the arbitrarily oriented bounding box as four scale offsets and angles in four quadrant sectors of the corresponding Cartesian coordinate system. Then, we divided the scales and angle space into multiple discrete sectors and obtained more accurate localization information by a coarse-granularity classification to fine-grained regression strategy. In addition, to decrease the angular-sector classification loss and accelerate the network’s convergence, we designed a smooth angular-sector label (SASL) that smoothly distributes label values with a definite tolerance radius. Finally, we proposed a localization-aided detection score (LADS) to better represent the confidence of a detected box by combining the category-classification score and the sector-selection score. The proposed MSO2-Det achieves state-of-the-art results on three widely used benchmarks, including the DOTA, HRSC2016, and UCAS-AOD data sets.

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“…Through modifying the GAN loss and penalty loss, the training process detection rate is significantly improved. Hu et al [ 170 ] embedded a sequence-PCA (Principal Component Analysis) layer and designed a new attention block to a deep learning network for automated thermography defects detection. The tested results verify that the proposed model can capture semantic information better and improve the detection rate in an end-to-end procedure.…”

Section: Data Managementmentioning

confidence: 99%

Pipeline In-Line Inspection Method, Instrumentation and Data Management

Qiu

Tian

Zeng

et al. 2021

Sensors

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Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

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A Lightweight Spatial and Temporal Multi-Feature Fusion Network for Defect Detection

Cited by 64 publications

References 31 publications

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

Multi-Sector Oriented Object Detector for Accurate Localization in Optical Remote Sensing Images

Pipeline In-Line Inspection Method, Instrumentation and Data Management

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