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Journal articles on the topic 'Mask RCNN'

Author: Grafiati

Published: 10 September 2021

Last updated: 29 July 2025

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1

Albar, Albar, Hendrick Hendrick, and Rahmad Hidayat. "Segmentation Method for Face Modelling in Thermal Images." Knowledge Engineering and Data Science 3, no. 2 (2020): 99. http://dx.doi.org/10.17977/um018v3i22020p99-105.

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Face detection is mostly applied in RGB images. The object detection usually applied the Deep Learning method for model creation. One method face spoofing is by using a thermal camera. The famous object detection methods are Yolo, Fast RCNN, Faster RCNN, SSD, and Mask RCNN. We proposed a segmentation Mask RCNN method to create a face model from thermal images. This model was able to locate the face area in images. The dataset was established using 1600 images. The images were created from direct capturing and collecting from the online dataset. The Mask RCNN was configured to train with 5 epoc

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Fizaine, Florian Côme, Patrick Bard, Michel Paindavoine, et al. "Historical Text Line Segmentation Using Deep Learning Algorithms: Mask-RCNN against U-Net Networks." Journal of Imaging 10, no. 3 (2024): 65. http://dx.doi.org/10.3390/jimaging10030065.

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Text line segmentation is a necessary preliminary step before most text transcription algorithms are applied. The leading deep learning networks used in this context (ARU-Net, dhSegment, and Doc-UFCN) are based on the U-Net architecture. They are efficient, but fall under the same concept, requiring a post-processing step to perform instance (e.g., text line) segmentation. In the present work, we test the advantages of Mask-RCNN, which is designed to perform instance segmentation directly. This work is the first to directly compare Mask-RCNN- and U-Net-based networks on text segmentation of hi

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Patekar, Rahul, Prashant Shukla Kumar, Hong-Seng Gan, and Muhammad Hanif Ramlee. "Automated Knee Bone Segmentation and Visualisation Using Mask RCNN and Marching Cube: Data From The Osteoarthritis Initiative." ASM Science Journal 17 (April 13, 2022): 1–7. http://dx.doi.org/10.32802/asmscj.2022.968.

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In this work, an automated knee bone segmentation model is proposed. A mask region-based convolutional neural network (RCNN) algorithm is developed to segment the bone and reconstructed into 3D object by using Marching-Cube algorithm. The proposed method is divided into two stages. First, the Mask RCNN is introduced to segment subchondral knee bone from the input MRI sequence. In the second stage, the segmented output from Mask R-CNN is fed as input to the Marching cube algorithm for the 3D reconstruction of knee subchondral bone. The proposed method achieved high dice similarity scores for fe

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Zhou, Ming, Jue Wang, and Bo Li. "ARG-Mask RCNN: An Infrared Insulator Fault-Detection Network Based on Improved Mask RCNN." Sensors 22, no. 13 (2022): 4720. http://dx.doi.org/10.3390/s22134720.

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Traditional power equipment defect-detection relies on manual verification, which places a high demand on the verifier’s experience, as well as a high workload and low efficiency, which can lead to false detection and missed detection. The Mask of the regions with CNN features (Mask RCNN) deep learning model is used to provide a defect-detection approach based on the Mask RCNN of Attention, Rotation, Genetic algorithm (ARG-Mask RCNN), which employs infrared imaging as the data source to assess the features of damaged insulators. For the backbone network of Mask RCNN, the structure of Residual

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Li, Wei, Tengfei Zhu, Xiaoyu Li, Jianzhang Dong, and Jun Liu. "Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection." Agriculture 12, no. 7 (2022): 1065. http://dx.doi.org/10.3390/agriculture12071065.

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Insect pest management is one of the main ways to improve the crop yield and quality in agriculture and it can accurately and timely detect insect pests, which is of great significance to agricultural production. In the past, most insect pest detection tasks relied on the experience of agricutural experts, which is time-consuming, laborious and subjective. In rencent years, various intelligent methods have emerged for detection. This paper employs three frontier Deep Convolutional Neural Network (DCNN) models—Faster-RCNN, Mask-RCNN and Yolov5, for efficient insect pest detection. In addition,

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Zhang, Jiawei, Pingli Ma, Tao Jiang, et al. "SEM-RCNN: A Squeeze-and-Excitation-Based Mask Region Convolutional Neural Network for Multi-Class Environmental Microorganism Detection." Applied Sciences 12, no. 19 (2022): 9902. http://dx.doi.org/10.3390/app12199902.

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This paper proposes a novel Squeeze-and-excitation-based Mask Region Convolutional Neural Network (SEM-RCNN) for Environmental Microorganisms (EM) detection tasks. Mask RCNN, one of the most applied object detection models, uses ResNet for feature extraction. However, ResNet cannot combine the features of different image channels. To further optimize the feature extraction ability of the network, SEM-RCNN is proposed to combine the different features extracted by SENet and ResNet. The addition of SENet can allocate weight information when extracting features and increase the proportion of usef

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Liu, Mingwei. "Light-Weight Semantic Segmentation Based on Mask RCNN." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 385–89. http://dx.doi.org/10.62051/rmzg3907.

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Semantic segmentation is based on the image information to detect and identify various categories of objects and output these objects mask. Compared with object detection and image classification, semantic segmentation has a more accurate recognition effect, and has a wide range of applications in automatic driving and other fields. With the development of deep learning, semantic segmentation methods based on deep learning have achieved good results, such as mask rcnn. Nowadays, Mask RCNN already has a good ability to handle object segmentation and mask the object. However, the model needs to

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Wang, Shijie, Guiling Sun, Bowen Zheng, and Yawen Du. "A Crop Image Segmentation and Extraction Algorithm Based on Mask RCNN." Entropy 23, no. 9 (2021): 1160. http://dx.doi.org/10.3390/e23091160.

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The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework

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Sozio, Angelo, Vincenzo Mariano Scarrica, Angela Rizzo, et al. "Application of Direct and Indirect Methodologies for Beach Litter Detection in Coastal Environments." Remote Sensing 16, no. 19 (2024): 3617. http://dx.doi.org/10.3390/rs16193617.

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In this study, different approaches for detecting of beach litter (BL) items in coastal environments are applied: the direct in situ survey, an indirect image analysis based on the manual visual screening approach, and two different automatic segmentation and classification tools. One is a Mask-RCNN based-algorithm, already used in a previous work, but specifically improved in this study for multi-class analysis. Test cases were carried out at the Torre Guaceto Marine Protected Area (Apulia Region, southern Italy), using a novel dataset from images acquired in different coastal environments by

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Li, Yane, Ying Wang, Dayu Xu, Jiaojiao Zhang, and Jun Wen. "An Improved Mask RCNN Model for Segmentation of ‘Kyoho’ (Vitis labruscana) Grape Bunch and Detection of Its Maturity Level." Agriculture 13, no. 4 (2023): 914. http://dx.doi.org/10.3390/agriculture13040914.

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The ‘Kyoho’ (Vitis labruscana) grape is one of the mainly fresh fruits; it is important to accurately segment the grape bunch and to detect its maturity level for the construction of an intelligent grape orchard. Grapes in the natural environment have different shapes, occlusion, complex backgrounds, and varying illumination; this leads to poor accuracy in grape maturity detection. In this paper, an improved Mask RCNN-based algorithm was proposed by adding attention mechanism modules to establish a grape bunch segmentation and maturity level detection model. The dataset had 656 grape bunches o

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Hidayat, Rahmat, Hendrick, Riandini, Zhi-Hao Wang, and Horng Gwo-Jiun. "Mask RCNN Methods for Eyes Modelling." International Journal of Data Science 2, no. 2 (2021): 63–68. http://dx.doi.org/10.18517/ijods.2.2.63-68.2021.

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Object detection is one of Deep Learning section in Computer Vision. The application of computer vision is divided into image classification and object detection. Object detection have target to find specific object from an image. The application of object detection for security are face recognition, and face detection. Face detection have been developed for medical application to identify emotion from faces. In this research, we proposed an eye modelling by using Mask RCNN. The eye model was applied in real time detection combined with OpenCV. The dataset was created from online dataset and i

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Ahmed, Belal, T. Aaron Gulliver, and Saif alZahir. "Image splicing detection using mask-RCNN." Signal, Image and Video Processing 14, no. 5 (2020): 1035–42. http://dx.doi.org/10.1007/s11760-020-01636-0.

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Ding, Hongyu, Muhammad Ahsan Latif, Zain Zia, Muhammad Asif Habib, Muhammad Abdul Qayum, and Quancai Jiang. "Facial Mask Detection Using Image Processing with Deep Learning." Mathematical Problems in Engineering 2022 (August 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/8220677.

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Coronavirus disease 2019 (COVID-19) has a significant impact on human life. The novel pandemic forced humans to change their lifestyles. Scientists have broken through the vaccine in many countries, but the face mask is the only protection for public interaction. In this study, deep neural networks (DNN) have been employed to determine the persons wearing masks correctly. The faster region-based convolutional neural networks (RCNN) model has been used to train the data using graphics processing unit (GPU) device. To achieve our goals, we used a multiphase detection model: first, to label the f

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Zhang, Hongmei, Zhijie Li, Zishang Yang, et al. "Detection of the Corn Kernel Breakage Rate Based on an Improved Mask Region-Based Convolutional Neural Network." Agriculture 13, no. 12 (2023): 2257. http://dx.doi.org/10.3390/agriculture13122257.

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Real-time knowledge of kernel breakage during corn harvesting plays a significant role in the adjustment of operational parameters of corn kernel harvesters. (1) Transfer learning by initializing the DenseNet121 network with pre-trained weights for training and validating a dataset of corn kernels was adopted. Additionally, the feature extraction capability of DenseNet121 was improved by incorporating the attention mechanism of a Convolutional Block Attention Module (CBAM) and a Feature Pyramid Network (FPN) structure. (2) The quality of intact and broken corn kernels and their pixels were fou

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Huang, Caiping, Yongkang Zhou, and Xin Xie. "Intelligent Diagnosis of Concrete Defects Based on Improved Mask R-CNN." Applied Sciences 14, no. 10 (2024): 4148. http://dx.doi.org/10.3390/app14104148.

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With the rapid development of artificial intelligence, computer vision techniques have been successfully applied to concrete defect diagnosis in bridge structural health monitoring. To enhance the accuracy of identifying the location and type of concrete defects (cracks, exposed bars, spalling, efflorescence and voids), this paper proposes improvements to the existing Mask Region Convolution Neural Network (Mask R-CNN). The improvements are as follows: (i) The residual network (ResNet101), the backbone network of Mask R-CNN which has too many convolution layers, is replaced by the lightweight

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Vania, Malinda, and Deukhee Lee. "Intervertebral disc instance segmentation using a multistage optimization mask-RCNN (MOM-RCNN)." Journal of Computational Design and Engineering 8, no. 4 (2021): 1023–36. http://dx.doi.org/10.1093/jcde/qwab030.

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Abstract Lower back pain is one of the major global challenges in health problems. Medical imaging is rapidly taking a predominant position for the diagnosis and treatment of lower back abnormalities. Magnetic resonance imaging (MRI) is a primary tool for detecting anatomical and functional abnormalities in the intervertebral disc (IVD) and provides valuable data for both diagnosis and research. Deep learning methods perform well in computer visioning when labeled general image training data are abundant. In the practice of medical images, the labeled data or the segmentation data are produced

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Zhao, Xiong, Tao Zuo, and Xinyu Hu. "OFM-SLAM: A Visual Semantic SLAM for Dynamic Indoor Environments." Mathematical Problems in Engineering 2021 (April 8, 2021): 1–16. http://dx.doi.org/10.1155/2021/5538840.

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Most of the current visual Simultaneous Localization and Mapping (SLAM) algorithms are designed based on the assumption of a static environment, and their robustness and accuracy in the dynamic environment do not behave well. The reason is that moving objects in the scene will cause the mismatch of features in the pose estimation process, which further affects its positioning and mapping accuracy. In the meantime, the three-dimensional semantic map plays a key role in mobile robot navigation, path planning, and other tasks. In this paper, we present OFM-SLAM: Optical Flow combining MASK-RCNN S

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Saputra, Ferry, Ali Farhan, Michael Edbert Suryanto, et al. "Automated Cardiac Chamber Size and Cardiac Physiology Measurement in Water Fleas by U-Net and Mask RCNN Convolutional Networks." Animals 12, no. 13 (2022): 1670. http://dx.doi.org/10.3390/ani12131670.

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Water fleas are an important lower invertebrate model that are usually used for ecotoxicity studies. Contrary to mammals, the heart of a water flea has a single chamber, which is relatively big in size and with fast-beating properties. Previous cardiac chamber volume measurement methods are primarily based on ImageJ manual counting at systolic and diastolic phases which suffer from low efficiency, high variation, and tedious operation. This study provides an automated and robust pipeline for cardiac chamber size estimation by a deep learning approach. Image segmentation analysis was performed

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Su, Wen-Hao, Jiajing Zhang, Ce Yang, et al. "Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision." Remote Sensing 13, no. 1 (2020): 26. http://dx.doi.org/10.3390/rs13010026.

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In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breed

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Zhang, Di, Martin Gade, Wensheng Wang, and Haoran Zhou. "EddyDet: A Deep Framework for Oceanic Eddy Detection in Synthetic Aperture Radar Images." Remote Sensing 15, no. 19 (2023): 4752. http://dx.doi.org/10.3390/rs15194752.

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This paper presents a deep framework EddyDet to automatically detect oceanic eddies in Synthetic Aperture Radar (SAR) images. The EddyDet has been developed using the Mask Region with Convolutional Neural Networks (Mask RCNN) framework, incorporating two new branches: Edge Head and Mask Intersection over Union (IoU) Head. The Edge Head can learn internal texture information implicitly, and the Mask IoU Head improves the quality of predicted masks. A SAR dataset for Oceanic Eddy Detection (SOED) is specifically constructed to evaluate the effectiveness of the EddyDet model in detecting oceanic

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Li, Ye, Qian Wu, Hongwei Sun, and Xuewei Wang. "Research on Lung Nodule Detection Based on Improved Target Detection Network." Complexity 2020 (December 16, 2020): 1–7. http://dx.doi.org/10.1155/2020/6633242.

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Lung nodules are an early symptom of lung cancer. The earlier they are found, the more beneficial it is for treatment. However, in practice, Chinese doctors are likely to cause misdiagnosis. Therefore, deep learning is introduced, an improved target detection network is used, and public datasets are used to diagnose and identify lung nodules. This paper selects the Mask-RCNN network and uses the dense block structure of Densenet and the channel shuffle convolution method to improve the Mask-RCNN network. The experimental results prove that proposed algorithm is extremely effective.

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Wan, Meng, Guannan Zhong, Qingshuang Wu, Xin Zhao, Yuqin Lin, and Yida Lu. "CR-Mask RCNN: An Improved Mask RCNN Method for Airport Runway Detection and Segmentation in Remote Sensing Images." Sensors 25, no. 3 (2025): 657. https://doi.org/10.3390/s25030657.

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Airport runways, as the core part of airports, belong to vital national infrastructure, and the target detection and segmentation of airport runways in remote sensing images using deep learning methods have significant research value. Most of the existing airport target detection methods based on deep learning rely on horizontal bounding boxes for localization, which often contain irrelevant background information. Moreover, when detecting multiple intersecting airport runways in a single remote sensing image, issues such as false positives and false negatives are apt to occur. To address thes

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DALAI, RADHAMADHAB, and Kishore Kumar Senapati. "A MASK-RCNN Based Approach Using Scale Invariant Feature Transform Key points for Object Detection from Uniform Background Scene." Advances in Image and Video Processing 7, no. 5 (2019): 01–08. http://dx.doi.org/10.14738/aivp.75.6946.

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Object identification using deep learning in known environment gives a new dimension to the research area of computer vision based automation system. As it uses supervised learning technique using Convolution Neural Network (RCNN) it helps automation software tools and machines to detect and identify objects using vision based systems. One of RCNN technique known as Mask-RCNN has been applied in this proposed design and this paper presents a novel approach to object detection problem using Big Data storage for large set of features based data. Earlier work Faster Region-based CNN has led to th

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Abratenko, P., R. An, J. Anthony, et al. "Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN." Journal of Instrumentation 17, no. 09 (2022): P09015. http://dx.doi.org/10.1088/1748-0221/17/09/p09015.

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Abstract In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions

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LIU, QINGCHUAN, Azmi Ayub Muhammad, Fazlina Ahmat Ruslan, Mohd Nor Azmi Ab Patar, and Shuzlina Abdul-Rahman. "PARTIAL OCCLUSION OBJECT DETECTION BASED ON IMPROVED MASK-RCNN." International Journal of Software Engineering and Computer Systems 10, no. 1 (2024): 20–31. http://dx.doi.org/10.15282/ijsecs.10.1.2024.2.0120.

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In the grasping task of industrial robots, multi-target objects are often placed in disorder or even partially occlusion or stacked, which brings certain difficulties to visual detection such as accuracy and real-time. The traditional Mask-RCNN algorithm can achieve high detection accuracy in scene which the target objects are neatly placed, but in the complex scenarios such as disorderly placed or partially occlusion is still have space for improvement in accuracy and speed. Mask-RCNN introduces the mask head structure to achieve pixel level segmentation mask prediction, it achieves high dete

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Kulshreshtha, Medhasvi, Sushma S. Chandra, Princy Randhawa, Georgios Tsaramirsis, Adil Khadidos, and Alaa O. Khadidos. "OATCR: Outdoor Autonomous Trash-Collecting Robot Design Using YOLOv4-Tiny." Electronics 10, no. 18 (2021): 2292. http://dx.doi.org/10.3390/electronics10182292.

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This paper proposed an innovative mechanical design using the Rocker-bogie mechanism for resilient Trash-Collecting Robots. Mask-RCNN, YOLOV4, and YOLOv4-tiny were experimented on and analyzed for trash detection. The Trash-Collecting Robot was developed to be completely autonomous as it was able to detect trash, move towards it, and pick it up while avoiding any obstacles along the way. Sensors including a camera, ultrasonic sensor, and GPS module played an imperative role in automation. The brain of the Robot, namely, Raspberry Pi and Arduino, processed the data from the sensors and performe

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Tyas, Dyah Aruming, and Tri Ratnaningsih. "Analisis Segmentasi Sel Darah Merah berbasis Mask-RCNN." Journal of Informatics Information System Software Engineering and Applications (INISTA) 5, no. 1 (2022): 1–7. http://dx.doi.org/10.20895/inista.v5i1.766.

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Pengembangan Computer-aided diagnosis (CAD) pada bidang patologi klinik memiliki tantangan tersendiri. CAD pada bidang patologi klinik diharapkan dapat membantu proses pengamatan laboratorium. Salah satu tantangan pengembangan CAD tersebut adalah pada proses segmentasi sel darah merah. Segmentasi sel darah merah yang menempel biasanya menimbulkan kesalahan segmentasi berupa bentuk sel tidak utuh atau sel sama sekali tidak tersegmentasi. Kesalahan segmentasi akan berakibat pada kesalahan pengenalan jenis sel darah sehingga diperlukan metode yang tepat untuk proses segmentasi. Oleh sebab itu, pe

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Shaodan, Lin, Feng Chen, and Chen Zhide. "A Ship Target Location and Mask Generation Algorithms Base on Mask RCNN." International Journal of Computational Intelligence Systems 12, no. 2 (2019): 1134. http://dx.doi.org/10.2991/ijcis.d.191008.001.

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Park, Jinyoung, and Hoseok Moon. "Lightweight Mask RCNN for Warship Detection and Segmentation." IEEE Access 10 (2022): 24936–44. http://dx.doi.org/10.1109/access.2022.3149297.

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Aljiffry, Latifa, Hassanin Al-Barhamtoshy, Felwa Abukhodair, and Amani Jamal. "Arabic Historical Documents Layout Analysis using Mask RCNN." Procedia Computer Science 244 (2024): 453–60. http://dx.doi.org/10.1016/j.procs.2024.10.220.

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Yasir, Nof, Shahzad Anwar, and Muhammad Tahir Khan. "Machine Vision based Intelligent Breast Cancer Detection." Pakistan Journal of Engineering and Technology 5, no. 1 (2022): 1–10. http://dx.doi.org/10.51846/vol5iss1pp1-10.

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Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limita

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Zhang, Hai-Yan, Xin-Yu Xu, Xue-Fen Ma, Qi Zhu, and Li Peng. "Mask-RCNN recognition method of composite fold shape in ultrasound images." Acta Physica Sinica 71, no. 7 (2022): 074302. http://dx.doi.org/10.7498/aps.71.20212009.

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Wrinkle defects will be inevitably produced during composite manufacturing and the in-service life of composite structures. Because of their diverse morphological changes and small deformation, it is difficult to manually identify the wrinkle with important errors. In order to improve the inspection efficiency, a Mask-RCNN algorithm is proposed to detect and classify different forms of wrinkle defects in composites based on phased array images. Carbon fiber composite laminates are prepared first in different forms of wrinkle defects. Secondly, the ultrasonic phased array is used to collect ful

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Liu, Xiangpeng, Danning Wang, Yani Li, Xiqiang Guan, and Chengjin Qin. "Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting." Sensors 22, no. 23 (2022): 9270. http://dx.doi.org/10.3390/s22239270.

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Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor c

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Wang, Xiangzhi. "Precise Human Removal and Inpainting Using Mask RCNN and LaMa." Applied and Computational Engineering 2, no. 1 (2023): 180–99. http://dx.doi.org/10.54254/2755-2721/2/20220668.

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Sometimes people are not supposed to be in a photo for various purposes, but this is usually unavoidable. Therefore, in the postprocessing of the image, it can be solved by removing people from the picture without affecting the coherence and naturalness of the object and background in the photo. We propose a human removal method based on image instance segmentation and image inpainting. Firstly, we send an image into the image instance segmentation algorithm to obtain a mask covering the unwanted parts of the picture. Then we do dilatation on this mask to expand the mask region. Finally, the i

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Whitin, Priscilla, S. Sivakumar, M. Geetha, et al. "Mask FORD-NET: Efficient Detection of Digital Image Forgery using Hybrid REG-NET based Mask-RCNN." International journal of electrical and computer engineering systems 15, no. 10 (2024): 829–35. http://dx.doi.org/10.32985/ijeces.15.10.2.

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Digital image is a binary representation of visual data which provides a rapid method for analyzing large quantities of data. Furthermore, digital images are more vulnerable to fraud when distributed over an open channel via information and communication technology. However, the image data can be modified fraudulently by intruders using vulnerabilities in telecommunications infrastructure. To overcome these issues, this paper proposes a novel Mask-RCNN based Image FORgery Detection (Mask FORD-NET) which is developed for digital image forgery detection. Initially, the input image is passed beyo

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Xian, Peng-Fei, Lai-Man Po, Jing-Jing Xiong, Yu-Zhi Zhao, Wing-Yin Yu, and Kwok-Wai Cheung. "Mask-Pyramid Network: A Novel Panoptic Segmentation Method." Sensors 24, no. 5 (2024): 1411. http://dx.doi.org/10.3390/s24051411.

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In this paper, we introduce a novel panoptic segmentation method called the Mask-Pyramid Network. Existing Mask RCNN-based methods first generate a large number of box proposals and then filter them at each feature level, which requires a lot of computational resources, while most of the box proposals are suppressed and discarded in the Non-Maximum Suppression process. Additionally, for panoptic segmentation, it is a problem to properly fuse the semantic segmentation results with the Mask RCNN-produced instance segmentation results. To address these issues, we propose a new mask pyramid mechan

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Kaya, Aşır Yüksel. "Detection of Structural Damage After an Earthquake Using GIS and Remote Sensing Methods." Turkish Journal of Agriculture - Food Science and Technology 13, no. 3 (2025): 688–96. https://doi.org/10.24925/turjaf.v13i3.688-696.7474.

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Developments in Geographic Information Systems and Remote Sensing (RS) technologies and innovative approaches emerging in deep learning (DL) supported analysis methods have an important place in disaster research as in every field. Convolutional neural networks such as Mask RCNN, U-NET, one of the deep learning methods for disaster damage impact assessment and classification, have started to show successful results. However, high-resolution geospatial imagery and drones provide faster and more accurate detection of structural damage. In this study, damaged building detection was performed usin

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Chakraborty, Debjani, Projjal Sahoo, Argha Biswas, Sujaan Maitra, Sourav Saha, and Biswajit Halder. "A Two-stage CNN Based Computer Vision Framework for Automated Validation of Indian Bank Cheques." Journal of Information Assurance and Security 19, no. 4 (2024): 146–61. https://doi.org/10.2478/ias-2024-0011.

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Abstract Automated bank cheque processing is still considered a challenging task for computer vision researchers. This article proposes a two-stage deep learning-based end-to-end computer vision framework to validate an Indian bank cheque with respect to a few mistakes commonly found in manually entered handwritten fields. The proposed framework primarily works in two stages involving two separate Mask RCNN models to detect two common mistakes due to the absence of any key handwritten field or the presence of any overwritten/strike-through handwritten character in the bank cheque image. The fi

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Yue, Zhaoxin, Bing Yan, Huaizhi Liu, and Zhe Chen. "An Effective Method for Underwater Biological Multi-Target Detection Using Mask Region-Based Convolutional Neural Network." Water 15, no. 19 (2023): 3507. http://dx.doi.org/10.3390/w15193507.

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Underwater creatures play a vital role in maintaining the delicate balance of the ocean ecosystem. In recent years, machine learning methods have been developed to identify underwater biologicals in the complex underwater environment. However, the scarcity and poor quality of underwater biological images present significant challenges to the recognition of underwater biological targets, especially multi-target recognition. To solve these problems, this paper proposed an ensemble method for underwater biological multi-target recognition. First, the CutMix method was improved for underwater biol

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Tao, Chongben, Yufeng Jin, Feng Cao, Zufeng Zhang, Chunguang Li, and Hanwen Gao. "3D Semantic VSLAM of Indoor Environment Based on Mask Scoring RCNN." Discrete Dynamics in Nature and Society 2020 (October 20, 2020): 1–14. http://dx.doi.org/10.1155/2020/5916205.

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In view of existing Visual SLAM (VSLAM) algorithms when constructing semantic map of indoor environment, there are problems with low accuracy and low label classification accuracy when feature points are sparse. This paper proposed a 3D semantic VSLAM algorithm called BMASK-RCNN based on Mask Scoring RCNN. Firstly, feature points of images are extracted by Binary Robust Invariant Scalable Keypoints (BRISK) algorithm. Secondly, map points of reference key frame are projected to current frame for feature matching and pose estimation, and an inverse depth filter is used to estimate scene depth of

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Zhang, Chan, Jing Li, Jian Huang, and Shangjie Wu. "Computed Tomography Image under Convolutional Neural Network Deep Learning Algorithm in Pulmonary Nodule Detection and Lung Function Examination." Journal of Healthcare Engineering 2021 (October 22, 2021): 1–9. http://dx.doi.org/10.1155/2021/3417285.

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The objective of this study was to perform segmentation and extraction of CT images of pulmonary nodules based on convolutional neural networks (CNNs). The Mask-RCNN algorithm model is a typical end-to-end image segmentation model, which uses the R-FCN structure for nodule detection. The effect of applying the two algorithm models to the computed tomography (CT) diagnosis of pulmonary nodules was analyzed, and different indexes of pulmonary nodule CT images in lung function examination after algorithm optimization were compared. A total of 56 patients diagnosed with pulmonary nodules by surger

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Hsia, Wei Ping, Siu Lun Tse, Chia Jen Chang, and Yu Len Huang. "Automatic Segmentation of Choroid Layer Using Deep Learning on Spectral Domain Optical Coherence Tomography." Applied Sciences 11, no. 12 (2021): 5488. http://dx.doi.org/10.3390/app11125488.

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The purpose of this article is to evaluate the accuracy of the optical coherence tomography (OCT) measurement of choroidal thickness in healthy eyes using a deep-learning method with the Mask R-CNN model. Thirty EDI-OCT of thirty patients were enrolled. A mask region-based convolutional neural network (Mask R-CNN) model composed of deep residual network (ResNet) and feature pyramid networks (FPNs) with standard convolution and fully connected heads for mask and box prediction, respectively, was used to automatically depict the choroid layer. The average choroidal thickness and subfoveal choroi

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Shi Heng Siow, Abu Ubaidah Shamsudin, Zubair Adil Soomro, et al. "Instance Segmentation Evaluation For Traffic Signs." Journal of Advanced Research in Applied Sciences and Engineering Technology 34, no. 2 (2023): 327–41. http://dx.doi.org/10.37934/araset.34.2.327341.

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This research paper focuses on developing a traffic sign recognition system based on the You Only Look At Coefficients (YOLACT) model, a one-stage instance segmentation model that offers high performance in terms of accuracy and reliability. However, the performance of YOLACT is influenced by various conditions such as day/night and different angles of objects. Therefore, this study aims to evaluate the impact of different angles and environments on the performance of the system. The paper discusses the framework, backbone structure, prototype generation branch, mask coefficient, and mask asse

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Chen, Jiajia, and Baocan Zhang. "Segmentation of Overlapping Cervical Cells with Mask Region Convolutional Neural Network." Computational and Mathematical Methods in Medicine 2021 (October 4, 2021): 1–10. http://dx.doi.org/10.1155/2021/3890988.

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The task of segmenting cytoplasm in cytology images is one of the most challenging tasks in cervix cytological analysis due to the presence of fuzzy and highly overlapping cells. Deep learning-based diagnostic technology has proven to be effective in segmenting complex medical images. We present a two-stage framework based on Mask RCNN to automatically segment overlapping cells. In stage one, candidate cytoplasm bounding boxes are proposed. In stage two, pixel-to-pixel alignment is used to refine the boundary and category classification is also presented. The performance of the proposed method

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Naufal, Ahmad, Chastine Fatichah, and Nanik Suciati. "Preprocessed Mask RCNN for Parking Space Detection in Smart Parking Systems." International Journal of Intelligent Engineering and Systems 13, no. 6 (2020): 255–65. http://dx.doi.org/10.22266/ijies2020.1231.23.

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This research developed a smart parking system through video data analysis using deep learning techniques that automatically determine the availability of vacant parking spaces. This system has two main stages. The first is the stage of marking the parking position on the image of a parking lot captured by the camera. This research proposes a Preprocessed Region-based Convolutional Neural Network (Mask R-CNN) to mark the parking position on the input image of a full parking lot. The preprocess that combining contrast enhancement using the Exposure Fusion framework, aims to overcome the problem

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Monchot, Paul, Loïc Coquelin, Khaled Guerroudj, et al. "Deep Learning Based Instance Segmentation of Titanium Dioxide Particles in the Form of Agglomerates in Scanning Electron Microscopy." Nanomaterials 11, no. 4 (2021): 968. http://dx.doi.org/10.3390/nano11040968.

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The size characterization of particles present in the form of agglomerates in images measured by scanning electron microscopy (SEM) requires a powerful image segmentation tool in order to properly define the boundaries of each particle. In this work, we propose to use an algorithm from the deep statistical learning community, the Mask-RCNN, coupled with transfer learning to overcome the problem of generalization of the commonly used image processing methods such as watershed or active contour. Indeed, the adjustment of the parameters of these algorithms is almost systematically necessary and s

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Mubarak, Auwalu Saleh, Zubaida Said Ameen, and Fadi Al-Turjman. "Effect of Gaussian filtered images on Mask RCNN in detection and segmentation of potholes in smart cities." Mathematical Biosciences and Engineering 20, no. 1 (2022): 283–95. http://dx.doi.org/10.3934/mbe.2023013.

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<abstract> <p>Accidents have contributed a lot to the loss of lives of motorists and serious damage to vehicles around the globe. Potholes are the major cause of these accidents. It is very important to build a model that will help in recognizing these potholes on vehicles. Several object detection models based on deep learning and computer vision were developed to detect these potholes. It is very important to develop a lightweight model with high accuracy and detection speed. In this study, we employed a Mask RCNN model with ResNet-50 and MobileNetv1 as the backbone to improve de

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Li, Xinhai, Chenxu Meng, Xing Xiao, Chao Yan, and Yuede Lin. "Study on Intelligent Image Recognition of Non-linear Short Pointer SF6 Meter Readings." E3S Web of Conferences 299 (2021): 03007. http://dx.doi.org/10.1051/e3sconf/202129903007.

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Currently widely used SF6 inflatable equipment pressure gauge are short pointer and the pointer is not connected to the center of the dial (non-linear pointer), The position of the hands on this dial cannot be identified using traditional computer vision techniques. In order to solve the problem, This paper proposes a method for SF6 pressure gauge pointer and reading recognition based on digital image processing and Mask-RCNN neural network image segmentation technology. The method first pre-processes the SF6 pressure gauge image and Canny edge detection, while using Mask-RCNN network to extra

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Song, Jiang, Jianguo Qian, Zhengjun Liu, et al. "Research on Arc Sag Measurement Methods for Transmission Lines Based on Deep Learning and Photogrammetry Technology." Remote Sensing 15, no. 10 (2023): 2533. http://dx.doi.org/10.3390/rs15102533.

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Arc sag is an important parameter in the design and operation and maintenance of transmission lines and is directly related to the safety and reliability of grid operation. The current arc sag measurement method is inefficient and costly, which makes it difficult to meet the engineering demand for fast inspection of transmission lines. In view of this, this paper proposes an automatic spacer bar segmentation algorithm, CM-Mask-RCNN, that combines the CAB attention mechanism and MHSA self-attention mechanism, which automatically extracts the spacer bars and calculates the center coordinates, an

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Hameed, Khurram, Douglas Chai, and Alexander Rassau. "Score-based mask edge improvement of Mask-RCNN for segmentation of fruit and vegetables." Expert Systems with Applications 190 (March 2022): 116205. http://dx.doi.org/10.1016/j.eswa.2021.116205.

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