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Volume 2, Issue 1 - Table of Contents
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On the cover: In this study, a novel Multi-Feature Extraction Network and Graph Fusion Detection Network (MFEn-GFDn) is introduced to improve radar target detection in complex environments like sea clutter. Unlike traditional single-feature detection methods, MFEn-GFDn leverages the complementarity of multiple features extracted from various time-frequency maps of radar signals, forming Multi-Feature Graph Data (MFG). GFDn then fuses these multi-feature representations for enhanced detection. Experimental results show that MFEn-GFDn improves detection probability by approximately 8% compared to the Dual-Channel Convolutional Neural Network (DCCNN), and further boosts performance, especially in scenarios with limited training data, by expanding the feature dimension. This approach demonstrates a significant advancement in radar detection capabilities.
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Open Access | Research Article | 29 March 2025
An Improved YOLOv8-Based Detection Model for Multi-Scale Sea Ice in Satellite Imagery
by
Yang Liu
,
Qiang Guo
,
Chengguo Dong
,
Yiping Luo
Chinese Journal of Information Fusion | Volume 2, Issue 1: 79-99, 2025 | DOI: 10.62762/CJIF.2025.695812
Abstract
Sea ice detection is of vital importance for maritime navigation. Satellite imagery is a crucial medium for conveying information about sea ice. Currently, most sea ice detection models mainly rely on texture information to identify sea ice in satellite imagery, while ignoring sea ice size information. This research presents an improved YOLOv8-Based detection algorithm for multi-scale sea ice. First, we propose a fusion module based on the attention mechanism and use it to replace the Concat module in the YOLOv8 network structure. Second, we conduct an applicability analysis of the bounding box regression loss function in YOLOv8 and ultimately select Shape-IoU, which is more suitable for sea... More >
Graphical Abstract
An Improved YOLOv8-Based Detection Model for Multi-Scale Sea Ice in Satellite Imagery
Open Access | Research Article | 27 March 2025
A Few-shot Learning Method Using Relation Graph
by
Zijing Liu
,
Chenggang Wang
Chinese Journal of Information Fusion | Volume 2, Issue 1: 70-78, 2025 | DOI: 10.62762/CJIF.2025.146072
Abstract
Few-shot learning aims to recognize new-class items under the circumstances with a few labeled support samples. However, many methods may suffer from poor guidance of limited new-class samples that are not suitable for being regarded as class centers. Recent works use word embedding to enrich the new-class distribution message but only use simple mapping between visual and semantic features during training. To solve the aforementioned problems, we propose a method that constructs a class relation graph by semantic meaning as guidance for feature extraction and fusion, to help the learning of the second-order relation information, with a light training request. In addition, we introduce two w... More >
Graphical Abstract
A Few-shot Learning Method Using Relation Graph
Open Access | Research Article | Feature Paper | 26 March 2025
Radar Multi-Feature Graph Representation and Graph Network Fusion Target Detection Methods
by
Ningyuan Su
,
Xiaolong Chen
,
Jian Guan
,
Xinghai Wang
,
Liangjiang Zhou
,
Jinhao Wang
,
Hongyong Wang
Chinese Journal of Information Fusion | Volume 2, Issue 1: 59-69, 2025 | DOI: 10.62762/CJIF.2025.413277
Abstract
In the context of neural network-based radar feature extraction and detection methods, single-feature detection approaches exhibit limited capability in distinguishing targets from background in complex environments such as sea clutter. To address this, a Multi-Feature Extraction Network and Graph Fusion Detection Network (MFEn-GFDn) method is proposed, leveraging feature complementarity and enhanced information utilization. MFEn extracts features from various time-frequency maps of radar signals to construct Multi-Feature Graph Data (MFG) for multi-feature graphical representation. Subsequently, GFDn performs fusion detection on MFG containing multi-feature information. By expanding the fea... More >
Graphical Abstract
Radar Multi-Feature Graph Representation and Graph Network Fusion Target Detection Methods
Open Access | Research Article | 22 March 2025
A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications
by
Xuebo Jin
,
Heran Fu
,
Jianlei Kong
,
Huijun Ma
,
Yuting Bai
,
Tingli Su
Chinese Journal of Information Fusion | Volume 2, Issue 1: 38-58, 2025 | DOI: 10.62762/CJIF.2025.919344
Abstract
With the progressive advancement of remote sensing image technology, its application in the agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing remote sensing images to ensure adequate food supply. However, such images often exist in harsh environments with many gaps and dense distribution, which poses major challenges to traditional target detection methods. The frequent missed detections and inaccurate bounding boxes severely constrain the further analysis and application of remote sensing images within the agricultural sector. This study presents an enhanced version of the YOLO algorithm, specifically tai... More >
Graphical Abstract
A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications
Open Access | Research Article | 20 March 2025
Integrating Relationship Path and Entity Neighbourhood Information for Knowledge Graph Intelligence of Social Things
by
Mohammad Shabaz
,
Mukesh Soni
Chinese Journal of Information Fusion | Volume 2, Issue 1: 27-37, 2025 | DOI: 10.62762/CJIF.2025.197460
Abstract
In the evolving framework of the Intelligence of Social Things (IoST), which amalgamates social networks and IoT ecosystems, knowledge graphs are essential for facilitating networked systems to efficiently process and leverage intricate relational data. Knowledge graphs offer essential technical assistance for various artificial intelligence applications, such as e-commerce, intelligent navigation, healthcare, and social media. Nonetheless, current knowledge graphs frequently lack completeness, harboring a considerable quantity of implicit knowledge that remains to be revealed. Consequently, tackling the difficulty of finalising knowledge graphs has emerged as a pressing research priority. M... More >
Graphical Abstract
Integrating Relationship Path and Entity Neighbourhood Information for Knowledge Graph Intelligence of Social Things
Open Access | Research Article | 17 March 2025
Quantitative Evaluation Method for Anomaly Levels of Complex Flight Maneuver Based on Multi-sensor Data
by
Liqiang Ren
,
Haipeng Wang
,
Xinlong Pan
,
Shuyi Jia
,
Bing Wan
Chinese Journal of Information Fusion | Volume 2, Issue 1: 14-26, 2025 | DOI: 10.62762/CJIF.2024.344084
Abstract
The methods that identify complex flight maneuvers from multi-sensor flight parameter data and conduct automated quantitative evaluations of anomaly levels could play an important role in enhancing flight safety and pilot training. However, existing methods focus on anomaly detection at individual flight parameter data points, making it challenging to accurately quantify the overall abnormality of a flight maneuver. To address this issue, this paper proposes a novel method for the quantitative evaluation of anomaly levels in complex flight maneuvers by fusing multi-sensor data. The proposed method comprises two stages: complex flight maneuver recognition and anomaly level quantification. In... More >
Graphical Abstract
Quantitative Evaluation Method for Anomaly Levels of Complex Flight Maneuver Based on Multi-sensor Data
Open Access | Research Article | 23 January 2025
Intelligent System Architecture Based on System Theory
by
Fuhu Chen
,
Zhe Wang
Chinese Journal of Information Fusion | Volume 2, Issue 1: 1-13, 2025 | DOI: 10.62762/CJIF.2024.872211
Abstract
Intelligent system is a research field that attracts much attention at present. Most of the researches on intelligent system focus on intelligent technology and its application. However, an intelligent system is first of all a system, which means it should have the characteristics of a system. Design of conventional system is mainly function- or task-oriented, and adaptation to environment is passive, static and regular. However, intelligent system is faced with a complex, random and dynamic environment, and has dynamic interaction with the environment. Behind this interaction behavior is a fusion of thinking and learning processes, and behind thinking learning is mission task and value. The... More >
Graphical Abstract
Intelligent System Architecture Based on System Theory