LEOD-Net: Learning Line-Encoded Bounding Boxes for Real-Time Object Detection

Ibrahem, Hatem; Salem, Ahmed; Kang, Hyun‐Soo

doi:10.3390/s22103699

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…All the previously mentioned methods employ a complex implementation of the encoder and decoder networks, while the proposed method employs a simple encoder and decoder implementation however it outperforms the SOTA methods in semantic segmentation. The DS module is proved to be superior in feature decoding in dense prediction tasks (semantic segmentation and depth estimation) as it is applied in recent research [41]- [43].…”

Section: Related Workmentioning

confidence: 99%

SDDS-Net: Space and Depth Encoder-Decoder Convolutional Neural Networks for Real-Time Semantic Segmentation

Ibrahem,

Salem,

Kang

2023

IEEE Access

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In this paper, we propose novel convolutional encoder-decoder architectures for real-time semantic segmentation based on an image-to-image translation approach via the space-to-depth and depth-tospace modules. We present architectures that compress the spatial information of the image using the spaceto-depth (SD) instead of the commonly used pooling methods (Max-pooling and Average-pooling) or strided convolution approaches. The SD module can reduce the image size while preserving the spatial information of the image in the form of extra depth information, this approach is much better than the pooling approaches which introduce a loss in the information and the details of the image. We also propose a lightweight and simple decoder stage using the depth-to-space (DS) module which constructs a high-resolution dense prediction map from a large number of low-resolution feature maps. The proposed architectures are efficient in learning image classification and semantic segmentation with high accuracy and average processing speed. We trained and tested our proposed architectures on image classification (i.e. CIFAR10 and Tiny ImageNet), and indoor and outdoor benchmarks for semantic segmentation specifically NYU-depthV2 and CITYSCAPES. The proposed architectures could attain high accuracy in classification (94.28% on CIFAR10 and 72.25% on Tiny ImageNet) and high mean average precision and pixel accuracy values in semantic segmentation (pixel accuracy of 78.55% on NYU-depthV2 and 87.9% on CITYSCAPES) while maintaining a real-time speed of frame processing outperforming recent state-of-the-art methods in semantic segmentation.

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Section: Related Workmentioning

confidence: 99%

SDDS-Net: Space and Depth Encoder-Decoder Convolutional Neural Networks for Real-Time Semantic Segmentation

Ibrahem,

Salem,

Kang

2023

IEEE Access

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“…It requires a more time-consuming supervised annotation procedure of the different objects in the image. CNNs use the dataset and identify every bounding box as an object class ( Ibrahem et al, 2022 ).…”

Section: Novel Diagnostic Tools By Using Image Analysis Techniquesmentioning

confidence: 99%

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Maturana

Oliveira²,

Nadal³

et al. 2022

Front. Microbiol.

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Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.

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Improved YOLOv8 Model for a Comprehensive Approach to Object Detection and Distance Estimation

Khow,

Tan,

Karim

et al. 2024

IEEE Access

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LEOD-Net: Learning Line-Encoded Bounding Boxes for Real-Time Object Detection

Cited by 3 publications

References 33 publications

SDDS-Net: Space and Depth Encoder-Decoder Convolutional Neural Networks for Real-Time Semantic Segmentation

SDDS-Net: Space and Depth Encoder-Decoder Convolutional Neural Networks for Real-Time Semantic Segmentation

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Improved YOLOv8 Model for a Comprehensive Approach to Object Detection and Distance Estimation

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