Building Detection From Aerial Imagery Using Inception Resnet Unet and Unet Architectures

Aghayari, S.; Hadavand, A.; Niazi, S. Mohamadnezhad; Omidalizarandi, M.

doi:10.5194/isprs-annals-x-4-w1-2022-9-2023

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…The IRv2-Net architecture is a modification of the UNet and the Inception ResNet [ 39 ]. The Inception architecture and residual blocks (RBs) are combined to create Inception ResNet [ 40 ]. IRv2-Net incorporates the InceptionResNetV2 model, a robust convolutional architecture for image processing, with the UNet architecture.…”

Section: Methodsmentioning

confidence: 99%

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IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

Ahamed,

Syfullah,

Sarkar

et al. 2023

Sensors

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Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development.

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Section: Methodsmentioning

confidence: 99%

“…A detailed representation of the Inception ResNet architecture is necessary to analyze the IRv2-Net architecture thoroughly. The Inception ResNet, which combines Inception blocks and RBs, serves as the foundation for the architecture [ 40 ]. These blocks incorporate CLs, MPs, and other operations.…”

Section: Methodsmentioning

confidence: 99%

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

Ahamed,

Syfullah,

Sarkar

et al. 2023

Sensors

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“…The deep neural network architecture Inception was introduced by Google in 2015 and is intended for tasks requiring picture recognition [13]. Inception V3 (GoogleNet V3) is based on a combination of convolutional layers of different sizes and pooling operations that extract features from the input image at different scales.…”

Section: Inception V3mentioning

confidence: 99%

Ensemble Deep Learning for Wear Particle Image Analysis

Shah,

Sridharan,

Mahanta

et al. 2023

Lubricants

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This technical note focuses on the application of deep learning techniques in the area of lubrication technology and tribology. This paper introduces a novel approach by employing deep learning methodologies to extract features from scanning electron microscopy (SEM) images, which depict wear particles obtained through the extraction and filtration of lubricating oil from a 4-stroke petrol internal combustion engine following varied travel distances. Specifically, this work postulates that the amalgamation of ensemble deep learning, involving the combination of multiple deep learning models, leads to greater accuracy compared to individually trained techniques. To substantiate this hypothesis, a fusion of deep learning methods is implemented, featuring deep convolutional neural network (CNN) architectures including Xception, Inception V3, and MobileNet V2. Through individualized training of each model, accuracies reached 85.93% for MobileNet V2 and 93.75% for Inception V3 and Xception. The major finding of this study is the hybrid ensemble deep learning model, which displayed a superior accuracy of 98.75%. This outcome not only surpasses the performance of the singularly trained models, but also substantiates the viability of the proposed hypothesis. This technical note highlights the effectiveness of utilizing ensemble deep learning methods for extracting wear particle features from SEM images. The demonstrated achievements of the hybrid model strongly support its adoption to improve predictive analytics and gain insights into intricate wear mechanisms across various engineering applications.

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Deep Learning Models for Building and Shadow Detection in Aerial Imagery: A Comparative Study

Naajim,

Rani,

Chugh

2024

Communications in Computer and Information Science

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Building Detection From Aerial Imagery Using Inception Resnet Unet and Unet Architectures

Cited by 6 publications

References 45 publications

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

Ensemble Deep Learning for Wear Particle Image Analysis

Deep Learning Models for Building and Shadow Detection in Aerial Imagery: A Comparative Study

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