Image splicing detection using mask-RCNN

Ahmed, Belal; Gulliver, T. Aaron; alZahir, Saif

doi:10.1007/s11760-020-01636-0

Cited by 72 publications

(27 citation statements)

References 26 publications

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“…A detailed description of the Mask R-CNN technique is given elsewhere (e.g., see, Kazimi et al, 2019). Because of its relatively easy trainability and efficiency, Mask R-CNN has seen a surge in popularity for use in object detection (Ahmed et al, 2020;Johnson, 2018;Sorokin, 2018;Yu et al, 2019) and in the analysis of archaeological airborne laser scanning (ALS) data (Gong & Zhang, 2020;Pham & Lefèvre, 2018;Verschoof-van der Vaart & Lambers, 2019).…”

Section: Introductionmentioning

confidence: 99%

A modified Mask region‐based convolutional neural network approach for the automated detection of archaeological sites on high‐resolution light detection and ranging‐derived digital elevation models in the North German Lowland

Bonhage

Eltaher

Raab

et al. 2021

Archaeological Prospection

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Due to complicated backgrounds and unclear target orientation, automated object detection is difficult in the field of archaeology. Most of the current convolutional neural network (CNN) object-oriented detection techniques are based on a faster region-based CNN (R-CNN) and other one-stage detectors that often lack adequate processing speeds and detection accuracies. Recently, the two-stage detector Mask R-CNN technique achieved impressive results in object detection and instance segmentation problems and was successfully applied in the analysis of archaeological airborne laser scanning (ALS) data. In this study, we outline a modified Mask R-CNN technique that reliably and efficiently detects relict charcoal hearth (RCH) sites on light detection and ranging (LiDAR) data-based digital elevation models (DEMs).Using image augmentation and image preprocessing steps combined with the deep learning-based adaptive gradient method with a dynamic bound on the learning rate (AdaBound) optimization technique, we could improve the model's accuracy and significantly reduce its training time. We use DEMs based on high-resolution LiDAR data and the visualization for archaeological topography (VAT) technique that give images with a very strong contrast of the terrain and the outline of the sites of interest in the North German Lowland. Therefore, the model can identify RCH sites with an average recall of 83% and an average precision of 87%. Techniques such as the modified Mask R-CNN method outlined here will help to greatly improve our knowledge about archaeological site densities in the realm of historical charcoal production and past human-landscape interactions. This method provides an accurate, time-efficient and bias-free large-scale site mapping option not only for the North German Lowland but potentially for other landscapes as well.

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Section: Introductionmentioning

confidence: 99%

A modified Mask region‐based convolutional neural network approach for the automated detection of archaeological sites on high‐resolution light detection and ranging‐derived digital elevation models in the North German Lowland

Bonhage

Eltaher

Raab

et al. 2021

Archaeological Prospection

View full text Add to dashboard Cite

show abstract

“…As for the model training, random numbers are generally used to initialize the weight matrices and the bias values to ensure that each parameter is not repeated and the difference is not large. In this study, the he_normal in [34] is used to achieve this work, so that the data has a good constant variance when input to the first convolution layer. Moreover, the L2 regularization in [35] is selected at the output layer to accelerate the convergence speed of the network and prevent the network overfitting.…”

Section: A Model Architecturementioning

confidence: 99%

A Novel Fault Identification Method for Photovoltaic Array via Convolutional Neural Network and Residual Gated Recurrent Unit

Gao

Wai

2020

IEEE Access

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Under the background of the large-scale construction of photovoltaic (PV) power stations, it is crucial to discover and solve module failures in time for improving the service life and maintaining the normal operation efficiency of modules. Based on analyzing the difference of I-V curves of PV arrays under different fault states, the I-V curves, temperatures and irradiances are taken as input data, and a fusion model of convolutional neural network (CNN) and residual-gated recurrent unit (Res-GRU) is proposed to identify the PV array fault. This model consists of a 1-dimensional CNN module with a 4-layer structure and a Res-GRU module. It has the advantages of end-to-end fault diagnosis, no manual feature extraction, strong anti-interference ability, and usable in the absence of irradiances and temperatures. Moreover, it can not only identify a single fault (e.g., short circuit, partial shading, abnormal aging, etc.), but also can effectively identify hybrid faults. Experimental results show that the classification accuracy of the proposed method is 98.61%, which is better than the ones of the artificial neural network (ANN), the extreme learning machine with kernel function (KELM), the fuzzy C-mean (FCM) clustering, the residual neural network (ResNet), and the stage-wise additive modeling using multi-class exponential loss function based on the classification and regression tree (SAMME-CART). In addition, in the absence of temperatures and irradiances, the classification accuracy still reaches 95.23%, which has a broad application prospect in the online fault diagnoses of PV arrays. INDEX TERMS Photovoltaic, fault diagnosis, I-V curve, 1-dimensional convolutional neural network (1-D CNN), residual-gated recurrent unit (Res-GRU)

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“…One involves candidate regions, and then the region of interest (ROI) is classified, and location coordinates are predicted. This kind of algorithm is called two-stage feature recognition algorithm [14].…”

Section: Dial Gray-scale Transformationmentioning

confidence: 99%

A Pointer Type Instrument Intelligent Reading System Design Based on Convolutional Neural Networks

2020

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The pointer instrument has the advantages of being simple, reliable, stable, easy to maintain, having strong anti-interference properties, and so on, which has long occupied the main position of electrical and electric instruments. Though the pointer instrument structure is simple, it is not convenient for real-time reading of measurements. In this paper, a RK3399 microcomputer was used for real-time intelligent reading of a pointer instrument using a camera. Firstly, a histogram normalization transform algorithm was used to optimize the brightness and enhance the contrast of images; then, the feature recognition algorithm You Only Look Once 3rd (YOLOv3) was used to detect and capture the panel area in images; and Convolutional Neural Networks were used to read and predict the characteristic images. Finally, predicted results were uploaded to a server. The system realized automatic identification, numerical reading, an intelligent online reading of pointer data, which has high feasibility and practical value. The experimental results show that the recognition rate of this system was 98.71% and the reading accuracy was 97.42%. What is more, the system can accurately locate the pointer-type instrument area and read corresponding values with simple operating conditions. This achievement meets the demand of real-time readings for analog instruments.

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Image splicing detection using mask-RCNN

Cited by 72 publications

References 26 publications

A modified Mask region‐based convolutional neural network approach for the automated detection of archaeological sites on high‐resolution light detection and ranging‐derived digital elevation models in the North German Lowland

A modified Mask region‐based convolutional neural network approach for the automated detection of archaeological sites on high‐resolution light detection and ranging‐derived digital elevation models in the North German Lowland

A Novel Fault Identification Method for Photovoltaic Array via Convolutional Neural Network and Residual Gated Recurrent Unit

A Pointer Type Instrument Intelligent Reading System Design Based on Convolutional Neural Networks

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