An infrared and visible image fusion algorithm based on ResNet-152

Zhang, Liming; Li, Heng; Zhu, Rui; Du, Ping

doi:10.1007/s11042-021-11549-w

Cited by 38 publications

(8 citation statements)

References 23 publications

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“…e classification effects of a total of seven representative CNN standard models [15][16][17][18][19][20][21] (all with a single optimizer and a single loss function) containing AlexNet, GoogleNet, ResNet, VGG, EfficientNet, Mobile-Net, and ShuffleNet are collected during the determination of the numerical model for weld seam recognition, and the classification accuracy of each model is shown in Table 2, where ResNet is the model with the highest accuracy for weld seam recognition among all algorithms in the table . Based on the ResNet model and using the driven strategy in Figure 2, the classification accuracy obtained was higher (1.6% improved) compared to the plain ResNet model. 3 is the confusion matrix of the "ResNet + multistage training strategy model."…”

Section: Classification Resultsmentioning

confidence: 99%

Research on Weld Seam Bead Recognition Based on Convolution Neural Network

Shi

Sun

Liu

et al. 2022

Scientific Programming

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In terms of the problems of five categories of nonweld seam stripes, including inclusion, oil-spot, silk-spot, and water-spot, which interfere with weld seam recognition during robotic welding, a convolutional neural network (CNN) algorithm, combined with a multistage training strategy, is used to construct a digital model for weld seam recognition, on the basis of which the classification accuracy is compared with the standard model of seven categories of representative CNN. The results show that the ResNet model with a multistage training strategy classifies weld seams with an accuracy of 83.8%, which is superior to other standard models. In this study, the physical scenario of weld seam recognition is migrated to a neural network digital model, fulfilling the intelligent recognition of weld seams in complex scenarios based on the CNN digital model.

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Section: Classification Resultsmentioning

confidence: 99%

Research on Weld Seam Bead Recognition Based on Convolution Neural Network

Shi

Sun

Liu

et al. 2022

Scientific Programming

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“…The ResNet152 [25][26][27] model is a critical deep-CNN architecture in CV-computer vision. Its inception can be traced back to the seminal research paper where its innovative design first took center stage.…”

Section: Resnetmentioning

confidence: 99%

AI-Driven Precision Clothing Classification: Revolutionizing Online Fashion Retailing with Hybrid Two-Objective Learning

Abbas,

Zhang,

Asim

et al. 2024

Information

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In the ever-expanding online fashion market, businesses in the clothing sales sector are presented with substantial growth opportunities. To utilize this potential, it is crucial to implement effective methods for accurately identifying clothing items. This entails a deep understanding of customer preferences, niche markets, tailored sales strategies, and an improved user experience. Artificial intelligence (AI) systems that can recognize and categorize clothing items play a crucial role in achieving these objectives, empowering businesses to boost sales and gain valuable customer insights. However, the challenge lies in accurately classifying diverse attire items in a rapidly evolving fashion landscape. Variations in styles, colors, and patterns make it difficult to consistently categorize clothing. Additionally, the quality of images provided by users varies widely, and background clutter can further complicate the task of accurate classification. Existing systems may struggle to provide the level of accuracy needed to meet customer expectations. To address these challenges, a meticulous dataset preparation process is essential. This includes careful data organization, the application of background removal techniques such as the GrabCut Algorithm, and resizing images for uniformity. The proposed solution involves a hybrid approach, combining the strengths of the ResNet152 and EfficientNetB7 architectures. This fusion of techniques aims to create a classification system capable of reliably distinguishing between various clothing items. The key innovation in this study is the development of a Two-Objective Learning model that leverages the capabilities of both ResNet152 and EfficientNetB7 architectures. This fusion approach enhances the accuracy of clothing item classification. The meticulously prepared dataset serves as the foundation for this model, ensuring that it can handle diverse clothing items effectively. The proposed methodology promises a novel approach to image identification and feature extraction, leading to impressive classification accuracy of 94%, coupled with stability and robustness.

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“…The ability to skip connections allows ResNet to develop a more flexible network. Adding a skip or shortcut connection to the output after a few weight layers helps overcome the issue of vanishing or exploding gradients (Zhang et al, 2022). The skip operation can be represented as in Equation ( 6).…”

Section: 𝐹(𝑥) = 𝑥 + 𝐺(𝑥)mentioning

confidence: 99%

Deep Residual Learning Image Recognition Model for Skin Cancer Disease Detection and Classification

Al-Tuwaijari¹,

Akhtar²,

Alhammadi³

et al. 2023

AIP

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Skin cancer is undoubtedly one of the deadliest diseases, and early detection of this disease can save lives. The usefulness and capabilities of deep learning in detecting and categorizing skin cancer based on images have been investigated in many studies. However, due to the variety of skin cancer tumour shapes and colours, deep learning algorithms misclassify whether a tumour is cancerous or benign. In this paper, we employed three different pre-trained state-of-the-art deep learning models: DenseNet121, VGG19 and an improved ResNet152, in classifying a skin image dataset. The dataset has a total of 3297 dermatoscopy images and two diagnostic categories: benign and malignant. The three models are supported by transfer learning and have been tested and evaluated based on the criteria of accuracy, loss, precision, recall, f1 score and ROC. Subsequently, the results show that the improved ResNet152 model significantly outperformed the other models and achieved an accuracy score of 92% and an ROC score of 91%. The DenseNet121 and VGG19 models achieve accuracy scores of 90% and 79% and ROC scores of 88% and 75%, respectively. Subsequently, a deep residual learning skin cancer recognition (ResNetScr) system has been implemented based on the ResNet152 model, and it has the capacity to help dermatologists in diagnosing skin cancer.

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An infrared and visible image fusion algorithm based on ResNet-152

Cited by 38 publications

References 23 publications

Research on Weld Seam Bead Recognition Based on Convolution Neural Network

Research on Weld Seam Bead Recognition Based on Convolution Neural Network

AI-Driven Precision Clothing Classification: Revolutionizing Online Fashion Retailing with Hybrid Two-Objective Learning

Deep Residual Learning Image Recognition Model for Skin Cancer Disease Detection and Classification

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