IECE Transactions on Emerging Topics in Artificial Intelligence

Free Access | Research Article | 20 November 2024

Automated Early Diabetic Retinopathy Detection Using a Deep Hybrid Model

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IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 71-83, 2024 | DOI:10.62762/TETAI.2024.305743

Abstract

Recently, the primary reason for blindness in adults has been diabetic retinopathy (DR) disease. Therefore, there is an increasing demand for a real-time efficient classification and detection system for diabetic retinopathy (DR) to overcome fast-growing disease (DR). We introduced a novel deep hybrid model for auto-mated diabetic retinopathy (DR) disease recognition and classification. Our model leverages the power of CNN architectures: Inception V3 and VGG16 models by combining their strengths to cater to exact requirements. VGG16 model efficiently captures fine features and wide-ranging features such as textures and edges, crucial for classifying initial signs of DR. Similarly, Inception... More >

Graphical Abstract

Automated Early Diabetic Retinopathy Detection Using a Deep Hybrid Model

Code (Data) Available | Free Access | Research Article | Feature Paper | 09 August 2024

LI3D-BiLSTM: A Lightweight Inception-3D Networks with BiLSTM for Video Action Recognition

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Fafa Wang

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Xuebo Jin

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Shenglun Yi

IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 58-70, 2024 | DOI:10.62762/TETAI.2024.628205

Abstract

This paper proposes an improved video action recognition method, primarily consisting of three key components. Firstly, in the data preprocessing stage, we developed multi-temporal scale video frame extraction and multi-spatial scale video cropping techniques to enhance content information and standardize input formats. Secondly, we propose a lightweight Inception-3D networks (LI3D) network structure for spatio-temporal feature extraction and design a soft-association feature aggregation module to improve the recognition accuracy of key actions in videos. Lastly, we employ a bidirectional LSTM network to contextualize the feature sequences extracted by LI3D, enhancing the representation capa... More >

Graphical Abstract

LI3D-BiLSTM: A Lightweight Inception-3D Networks with BiLSTM for Video Action Recognition

Free Access | Research Article | Feature Paper | 29 May 2024

CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes

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Ming Gao

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Shixin Yang

IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 44-57, 2024 | DOI:10.62762/TETAI.2024.240529

Abstract

In the era of rapid technological advancement, the demand for sophisticated Multi-Object Tracking (MOT) systems in applications such as intelligent surveillance and autonomous navigation has become increasingly critical.However, existing models often struggle with accuracy and efficiency in densely populated or dynamically complex environments. Addressing these challenges, we introduce a novel deep learning-based MOT model that incorporates the latest CT-DETR detection technology and an advanced ReID module for improved pedestrian tracking. Experimental results demonstrate the model's superior performance in accurately identifying and tracking multiple targets across varied scenarios, signif... More >

Graphical Abstract

CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes

Free Access | Research Article | Feature Paper | 21 May 2024 | Cited: 4

Improved Object Detection Algorithm Based on Multi-scale and Variability Convolutional Neural Networks

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Jiaxun Yang

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Yilihamujiang Gapar

IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 31-43, 2024 | DOI:10.62762/TETAI.2024.115892

Abstract

This paper proposes an improved object detection algorithm based on a dynamically deformable convolutional network (D-DCN), aiming to solve the multi-scale and variability challenges in object detection tasks. First, we review traditional methods in the field of object detection and introduce the current research status of improved methods based on multi-scale and variability convolutional neural networks. Then, we introduce in detail our proposed improved algorithms, including an improved feature pyramid network and a dynamically deformable network. In the improved feature pyramid network, we introduce a multi-scale feature fusion mechanism to better capture target information at different... More >

Graphical Abstract

Improved Object Detection Algorithm Based on Multi-scale and Variability Convolutional Neural Networks

Free Access | Research Article | Feature Paper | 20 April 2024 | Cited: 2

Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation

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Shaohuang Wang

IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 17-30, 2024 | DOI:10.62762/TETAI.2024.320179

Abstract

In this paper, we introduce a novel fast object detection framework, designed to meet the needs of real-time applications such as autonomous driving and robot navigation. Traditional processing methods often trade-off between accuracy and processing speed. To address this issue, we propose a hybrid data representation method that combines the computational efficiency of voxelization with the detail capture capability of direct data processing to optimize overall performance. Our detection framework comprises two main components: a Rapid Region Proposal Network (RPN) and a Refinement Detection Network (RefinerNet). The RPN is used to generate high-quality candidate regions, while the RefinerN... More >

Graphical Abstract

Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation

Free Access | Research Article | Feature Paper | 07 April 2024 | Cited: 10

YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems

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Ming Yang

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Xiangyu Fan

IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 1-16, 2024 | DOI:10.62762/TETAI.2024.894227

Abstract

With the rapid development of autonomous driving technology, the demand for real-time and efficient object detection systems has been increasing to ensure vehicles can accurately perceive and respond to the surrounding environment. Traditional object detection models often suffer from issues such as large parameter sizes and high computational resource consumption, limiting their applicability on edge devices. To address this issue, we propose a lightweight object detection model called YOLOv8-Lite, based on the YOLOv8 framework, and improved through various enhancements including the adoption of the FastDet structure, TFPN pyramid structure, and CBAM attention mechanism. These improvement... More >

Graphical Abstract

YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems

IECE Transactions on Emerging Topics in Artificial Intelligence

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