A Ship Target Location and Mask Generation Algorithms Base on Mask RCNN

Shaodan, Lin; Feng, Chun-Mei; Chen, Zhide

doi:10.2991/ijcis.d.191008.001

Cited by 19 publications

(7 citation statements)

References 18 publications

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“…Although Mask R‐CNN 64 is a state‐of‐the‐art method in the field of object detection which represents the high segmentation quality and the high accuracy and has been used in many different research areas, 65–72 its performance is not satisfactory in the dataset studied in this paper. Therefore, this article adopts the YOLACT 62 which won the COCO Challenge Innovation Award of ICCV2019, and it performs better than Mask R‐CNN on the dataset studied in this paper.…”

Section: Methodsmentioning

confidence: 93%

Automatic segmentation, inpainting, and classification of defective patterns on ancient architecture using multiple deep learning algorithms

Zou

Zhao

2021

Struct Control Health Monit

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Ancient architectures have a lot of distinctive patterns on their component surfaces. But due to the wind and the Sun, the patterns will present extensive deficiencies, which are not conducive to the routine inspections and the subsequent repair work. To overcome these limits, an automatic deep learning-based method of segmentation, inpainting, and classification for defective patterns on ancient architecture is proposed. First, You Only Look At CoefficienTs, which is a real-time instance segmentation network, is applied to obtain the mask of the defective parts. Then Generative Image Inpainting with Contextual Attention, which is an image inpainting algorithm, is used to reconstruct the defective parts. Finally, Residual Neural Network-50 is used to classify the reconstructed images. In this paper, three types of dragon motifs in the Forbidden City were studied. The results show that the classification accuracy of the reconstructed images is increased by an average of 13.1%, with the maximum increasing by 23.5%. The proposed methods can prepare for future routine inspections in advance.

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Section: Methodsmentioning

confidence: 93%

Automatic segmentation, inpainting, and classification of defective patterns on ancient architecture using multiple deep learning algorithms

Zou

Zhao

2021

Struct Control Health Monit

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“…Here,

is the label of a cell

in the true mask for the region of size

, and

is the predicted value of the same cell in the mask learned for the ground truth class k [ 43 ]. In addition to obtaining the confidence score, the SoftMax classifier (

) converted the score from the SoftMax calculation into probabilities [ 44 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning

et al. 2022

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An analysis of scar tissue is necessary to understand the pathological tissue conditions during or after the wound healing process. Hematoxylin and eosin (HE) staining has conventionally been applied to understand the morphology of scar tissue. However, the scar lesions cannot be analyzed from a whole slide image. The current study aimed to develop a method for the rapid and automatic characterization of scar lesions in HE-stained scar tissues using a supervised and unsupervised learning algorithm. The supervised learning used a Mask region-based convolutional neural network (RCNN) to train a pattern from a data representation using MMDetection tools. The K-means algorithm characterized the HE-stained tissue and extracted the main features, such as the collagen density and directional variance of the collagen. The Mask RCNN model effectively predicted scar images using various backbone networks (e.g., ResNet50, ResNet101, ResNeSt50, and ResNeSt101) with high accuracy. The K-means clustering method successfully characterized the HE-stained tissue by separating the main features in terms of the collagen fiber and dermal mature components, namely, the glands, hair follicles, and nuclei. A quantitative analysis of the scar tissue in terms of the collagen density and directional variance of the collagen confirmed 50% differences between the normal and scar tissues. The proposed methods were utilized to characterize the pathological features of scar tissue for an objective histological analysis. The trained model is time-efficient when used for detection in place of a manual analysis. Machine learning-assisted analysis is expected to aid in understanding scar conditions, and to help establish an optimal treatment plan.

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“…According to the total number of single cerebral edema pixels, RPN uses the SoftMax-Loss layer to train and classify the generated anchor points. The Smooth L 1 layer was used to modify the anchor point coordinates to avoid gradient explosion problems 24 .…”

Section: Methodsmentioning

confidence: 99%

Using deep learning models to analyze the cerebral edema complication caused by radiotherapy in patients with intracranial tumor

Chao

Chang

Kang

et al. 2022

Sci Rep

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Using deep learning models to analyze patients with intracranial tumors, to study the image segmentation and standard results by clinical depiction complications of cerebral edema after receiving radiotherapy. In this study, patients with intracranial tumors receiving computer knife (CyberKnife M6) stereotactic radiosurgery were followed using the treatment planning system (MultiPlan 5.1.3) to obtain before-treatment and four-month follow-up images of patients. The TensorFlow platform was used as the core architecture for training neural networks. Supervised learning was used to build labels for the cerebral edema dataset by using Mask region-based convolutional neural networks (R-CNN), and region growing algorithms. The three evaluation coefficients DICE, Jaccard (intersection over union, IoU), and volumetric overlap error (VOE) were used to analyze and calculate the algorithms in the image collection for cerebral edema image segmentation and the standard as described by the oncologists. When DICE and IoU indices were 1, and the VOE index was 0, the results were identical to those described by the clinician.The study found using the Mask R-CNN model in the segmentation of cerebral edema, the DICE index was 0.88, the IoU index was 0.79, and the VOE index was 2.0. The DICE, IoU, and VOE indices using region growing were 0.77, 0.64, and 3.2, respectively. Using the evaluated index, the Mask R-CNN model had the best segmentation effect. This method can be implemented in the clinical workflow in the future to achieve good complication segmentation and provide clinical evaluation and guidance suggestions.

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A Ship Target Location and Mask Generation Algorithms Base on Mask RCNN

Cited by 19 publications

References 18 publications

Automatic segmentation, inpainting, and classification of defective patterns on ancient architecture using multiple deep learning algorithms

Automatic segmentation, inpainting, and classification of defective patterns on ancient architecture using multiple deep learning algorithms

Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning

Using deep learning models to analyze the cerebral edema complication caused by radiotherapy in patients with intracranial tumor

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