Relevant bibliographies by topics / Mask RCNN

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Mask RCNN'

Author: Grafiati
Published: 10 September 2021
Last updated: 29 July 2025

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Journal articles on the topic "Mask RCNN"

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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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Dissertations / Theses on the topic "Mask RCNN"

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Hameed, Khurram. "Computer vision based classification of fruits and vegetables for self-checkout at supermarkets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2519.

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The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide r
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Lin, Po-Hung, and 林泊宏. "Wood Knots Classification and Detection by Modified Mask-RCNN." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/87f588.

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碩士<br>國立中興大學<br>統計學研究所<br>106<br>In this thesis we focus on two topics：(1) Classification of single wood knot image datasets. Here, the Xception convolutional neural network is used for image recognition.(2) Detection of defects on spruce wood by identifying the area of the mask and classifying the type of knots. Our idea is based on Mask Region based Convolution Neural Networks (Mask-RCNN). According to the nature of wood defects dataset, we modify the loss function in Mask-RCNN by replacing cross-entropy loss with focal loss as well as an additional tuning parameter to improve the detection
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Fang, Meng-Meng, and 房濛濛. "Mask RCNN for Liver Tumor Segmentation using Dynamic Computed Tomography." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/d3dhgf.

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Lin, Hsuan-Po, and 林鉉博. "Tumor area detection and instance segmentation of breast ultra-sound images using Mask-RCNN based on deep learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/243gwp.

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碩士<br>國立清華大學<br>電機工程學系所<br>106<br>The mortality rate of breast cancer among women in Taiwan ranks the top five in 2017 according to the statistics of the cause of death of the Ministry of Health and Welfare, Taiwan. Hence early screening/diagnosis via mammogram or ultrasound of breast cancer is vital. Due to the painful process of taking mammogram and fear of excess X-ray radiation exposure, ultrasound breast image examination become cheaper and well-accepted in breast diagnosis. Tumor segmentation of ultrasound breast (USB) images becomes important. Usually, traditional image segmentation met
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Lin, Yan-Liang. "Semi-automatic classification of tree species using a combination of RGB drone imagery and mask RCNN: case study of the Highveld region in Eswatini." Master's thesis, 2021. http://hdl.handle.net/10362/113903.

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies<br>Tree species identification forms an integral part of biodiversity monitoring. Locating at-risk species and predicting their distribution is equally as important as tracing invasive alien plant species distributions. The high prevalence of the latter and their destructive impact on the environment is the focus for this thesis. In areas of the world where technology limitations are restrictive, an approach using low-cost, available RGB drone imagery is proposed to t
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Book chapters on the topic "Mask RCNN"

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Johnson, Jeremiah W. "Automatic Nucleus Segmentation with Mask-RCNN." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17798-0_32.

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Wu, Xin, Shiguang Wen, and Yuan-ai Xie. "Improvement of Mask-RCNN Object Segmentation Algorithm." In Intelligent Robotics and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27526-6_51.

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Hariharan, R., M. Dhilsath Fathima, B. Prakash, S. Abi Sundar, E. Punith, and M. Mohammed Muzameel Hussain. "Real Time Video Instance Segmentation Using Mask RCNN." In Recent Trends in Computational Intelligence and Its Application. CRC Press, 2023. http://dx.doi.org/10.1201/9781003388913-50.

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Oubelkas, Farah, Lahcen Moumoun, and Abdellah Jamali. "Car Damage Detection Based on Mask Scoring RCNN." In International Conference on Advanced Intelligent Systems for Sustainable Development. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35251-5_36.

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Rakhi, Anusree Mondal, Arya P. Dhorajiya, and P. Saranya. "Enhanced Mask-RCNN for Ship Detection and Segmentation." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2541-2_16.

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Zaghbani, Soumaya, and Med Salim Bouhlel. "Mask RCNN for Human Motion and Actions Recognition." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73689-7_1.

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Han, Xuejing, Yongxu Li, Chengyi Duan, Ruixue He, and Hui Jin. "Mask-RCNN with Attention Mechanism for Detection and Segmentation." In Intelligent Robotics and Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6498-7_48.

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Tiwari, Vivek, Shailendra Gupta, Priyadarshini Roy, et al. "Soybean Crop Non-beneficial Insect Identification Using Mask RCNN." In Information and Communication Technology for Competitive Strategies (ICTCS 2020). Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0739-4_30.

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Kshatriya, Aditya, V. M. Nisha, and S. A. Sajidha. "Person Re-identification Using Deep Learning with Mask-RCNN." In Artificial Intelligence and Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6448-9_49.

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Aryan and Suman Deb. "Identification of Diabetic Retinopathy Using Robust Segmentation Through Mask RCNN." In Computational Intelligence in Pattern Recognition. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3734-9_4.

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Conference papers on the topic "Mask RCNN"

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Rani, N. Shobha, Akshatha Prabhu, Abhiram M P, and Adarsh V. "Bacterial Wilt Detection from Okra Leaf Using Mask-RCNN." In 2023 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE, 2023. http://dx.doi.org/10.1109/ccem60455.2023.00031.

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Ahire, Swapnil Karbhari, and Duan-Yu Chen. "Knowledge Distilled Mask-rcnn for Image Shadow Detection and Segmentation." In 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). IEEE, 2024. http://dx.doi.org/10.1109/icce-taiwan62264.2024.10674346.

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Shi, Yi, and Bokang Zhang. "Improved mask-RCNN remote sensing image extraction based on attention mechanism." In International Conference on Cloud Computing, Performance Computing, and Deep Learning, edited by Wanyang Dai and Xiangjie Kong. SPIE, 2024. http://dx.doi.org/10.1117/12.3050704.

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Sharma, Jatin, Deepak Kumar, and Raman Verma. "Deep Learning-Based Wheat Stripe Rust Disease Recognition Using Mask RCNN." In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA). IEEE, 2024. https://doi.org/10.1109/icisaa62385.2024.10829311.

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Wang, SaiWen. "Two-stage workpiece recognition algorithm based on Mask-RCNN and template matching." In Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), edited by Sumeet S. Aphale and Ajit Jha. SPIE, 2024. https://doi.org/10.1117/12.3055508.

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Jia, Zimu, Ye Zhang, and Huiya Yang. "Research on High-Precision Object Detection and Instance Segmentation Using Mask-RCNN." In 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2024. https://doi.org/10.1109/iccasit62299.2024.10827917.

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Sharma, Jatin, Deepak Kumar, and Raman Verma. "Deep Learning-Based Wheat Stem Rust Disease Recognition Using Mask Scoring RCNN." In 2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT). IEEE, 2024. https://doi.org/10.1109/ic-sit63503.2024.10862363.

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Jiao, Hongqiao. "An improved mask RCNN algorithm for abnormal identification of expressway traffic flow." In Fifth International Conference on Digital Signal and Computer Communications (DSCC 2025), edited by Gordana Jovanovic-Dolecek and Ke-Lin Du. SPIE, 2025. https://doi.org/10.1117/12.3071308.

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Talunga, Evianti, Indrabayu, and Ingrid Nurtanio. "Detection of Coffee Bean Defects on Conveyor Machines Using the Mask-RCNN Algorithm." In 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2024. http://dx.doi.org/10.1109/icitisee63424.2024.10730189.

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Yang, Zijia, Lina Wang, Long Wen, et al. "Efficient Scarab Identification via Multi-Source Data Fusion in Mask-RCNN with Attention Mechanism." In 2024 IEEE International Conference on Smart Internet of Things (SmartIoT). IEEE, 2024. https://doi.org/10.1109/smartiot62235.2024.00030.

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