Edge preserving noise robust deep learning networks for vehicle classification

Kiran, V. Keerthi; Dash, Sonali; Parida, Priyadarsan

doi:10.1002/cpe.7214

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…where W(t), f(t), and x(t) denote the output layer, the flter in the layer, and the inputted image, respectively. Convolutional layers create millions of neurons and need complicated computations and long execution times [39], Tus, CNN has pooling layers to solve this issue. Pooling layers decrease the dimensions of features through two methods: max and average pooling [40].…”

Section: Hybrid Approach Between Deep Learning and Svmmentioning

confidence: 99%

Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Al-Mekhlafi

Senan

Alshudukhi

et al. 2023

International Journal of Intelligent Systems

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Gastrointestinal (GI) diseases, particularly tumours, are considered one of the most widespread and dangerous diseases and thus need timely health care for early detection to reduce deaths. Endoscopy technology is an effective technique for diagnosing GI diseases, thus producing a video containing thousands of frames. However, it is difficult to analyse all the images by a gastroenterologist, and it takes a long time to keep track of all the frames. Thus, artificial intelligence systems provide solutions to this challenge by analysing thousands of images with high speed and effective accuracy. Hence, systems with different methodologies are developed in this work. The first methodology for diagnosing endoscopy images of GI diseases is by using VGG-16 + SVM and DenseNet-121 + SVM. The second methodology for diagnosing endoscopy images of gastrointestinal diseases by artificial neural network (ANN) is based on fused features between VGG-16 and DenseNet-121 before and after high-dimensionality reduction by the principal component analysis (PCA). The third methodology is by ANN and is based on the fused features between VGG-16 and handcrafted features and features fused between DenseNet-121 and the handcrafted features. Herein, handcrafted features combine the features of gray level cooccurrence matrix (GLCM), discrete wavelet transform (DWT), fuzzy colour histogram (FCH), and local binary pattern (LBP) methods. All systems achieved promising results for diagnosing endoscopy images of the gastroenterology data set. The ANN network reached an accuracy, sensitivity, precision, specificity, and an AUC of 98.9%, 98.70%, 98.94%, 99.69%, and 99.51%, respectively, based on fused features of the VGG-16 and the handcrafted.

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Section: Hybrid Approach Between Deep Learning and Svmmentioning

confidence: 99%

Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Al-Mekhlafi

Senan

Alshudukhi

et al. 2023

International Journal of Intelligent Systems

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show abstract

“…The conventional approaches lack robustness, generalization ability, and precision and are easily impacted by factors like lighting, weather, obstruction, and lack of visibility [14]. In general, training data and testing data are believed to be derived from comparable or equal distributions.…”

Section: Introductionmentioning

confidence: 99%

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

Keerthi Kiran,

Dash,

Parida

2024

Eng. Res. Express

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Traffic congestion is prevalent in many major and medium-sized cities throughout different countries in contemporary society. In traffic images, various multi-sized vehicles are tightly clustered together and obstructed from one another. Identifying vehicles in such instances is crucial for urban traffic surveillance, safety monitoring, and legal concerns but it also presents major challenges. The remarkable detection accuracy and efficiency of deep learning-based systems have led to their recent and extensive use in vehicle identification. There are significant advanced YOLO models with different backbone architectures and frameworks developed for vehicle detection. Yet, the performance of YOLO variants is facing the challenges of handling false detection against occluded and densely sophisticated scenarios. The proposed model is developed to address such types of limitations, for example; dynamic illumination, noisy images, and scale sensitivity to improve the vehicle detection rate in different traffic scenarios and varying weather conditions. The proposed study employs an improved YOLOv4 to identify moving vehicles in different lighting conditions including daylight, cloudy, rainy, and night. For hybridization, three techniques are utilized such as the Multiscale Retinex, Dual tree complex wavelet transform (DTCWT), and Pulse Coupled Neural Networks (PCNN). The DTCWT is employed for multiscale decomposition and to denoise the complex high frequency subband information, then the denoised subbands are reconstructed into a denoised image. The Multiscale retinex is utilized to reduce the halo artifacts on high-contrast edges and maintain the balance with dynamic range compression and color reproduction. The synchronizing pulse burst property of PCNN is used to detect the isolated noisy pixels and modify the detected noisy pixels. From the results it is worth noting that the developed model surpasses state-of-the-art methods in sunny, night, cloudy, and rainy modes. The proposed method using the DTCWT technique can detect the vehicles with mAP of 91.095% and 35FPS.

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RETRACTED ARTICLE: Illumination robust deep convolutional neural network for medical image classification

Dash

Parida²,

Mohanty³

2023

Soft Comput

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Edge preserving noise robust deep learning networks for vehicle classification

Cited by 6 publications

References 43 publications

Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

RETRACTED ARTICLE: Illumination robust deep convolutional neural network for medical image classification

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