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Online Multi-Object Tracking Under Moving Unmanned Aerial Vehicle Platform Based on Object Detection and Feature Extraction Network

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Journal of shanghai Jiaotong university(Science) 2024年第3期29卷 388-399页

作者：刘增敏王申涛姚莉秀蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China key laboratory of System Control and Information processing of Ministry of Education Shanghai 200240China shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China key laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China JD Business Growth Department Beijing 100176China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is ***,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking *** the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking *** simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.

关键词： moving unmanned aerial vehicle(UAV)platform small object feature extraction image registration multi-object tracking

A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

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computers, Materials & Continua 2024年第1期78卷 197-225页

作者： Xiaozhong Yu Jinhua Zheng College of computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial key laboratory of Intelligent Information processing and Application Hengyang Normal UniversityHengyang421002China

In rice production,the prevention and management of pests and diseases have always received special *** methods require human experts,which is costly and *** to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is ***,deep learning technology has been employed to detect and identify rice diseases and *** paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing ***,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and *** study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.

关键词： Deep learning rice diseases and pests image recognition object detection

A Transformer-assisted Cascade Learning Network for Choroidal Vessel Segmentation

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Journal of computer Science & Technology 2024年第2期39卷 286-304页

作者：温阳吴依林毕磊石武祯刘潇骁许毓鹏许迅曹文明冯大淦 Guangdong Provincial key laboratory of Intelligent Information processing School of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060China Institute of Translational Medicine Shanghai Jiao Tong UniversityShanghai 200240China Department of Ophthalmology Shanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai 200080China School of Information Technologies The University of SydneySydney 2006Australia

As a highly vascular eye part,the choroid is crucial in various eye disease ***,limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data,particularly for the choroidal ***,the existing direct choroidal vessel segmentation methods for the intelligent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data,while the synergistic segmentation methods compromise vessel segmentation performance for the choroid layer segmentation *** cascaded structures grapple with error propagation during *** address these challenges,we propose a cascade learning segmentation method for the inner vessel structures of the choroid in this ***,we propose Transformerassisted Cascade Learning Network(TACLNet)for choroidal vessel segmentation,which comprises a two-stage training strategy:pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel *** also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC,capturing differential and detailed information ***,we implement an auxiliary Transformer branch named ATB to integrate global features into the segmentation *** results exhibit that our method achieves the state-of-the-art performance for choroidal vessel ***,we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography(OCT)scans on a publicly available *** these fully prove that our TACLNet contributes to the advancement of choroidal vessel segmentation and is of great significance for ophthalmic research and clinical application.

关键词： choroidal vessel segmentation optical coherence tomography(OCT) Transformer-assisted cascade learning retinal fluid segmentation multi-scale feature extraction

Quantifying structural distortion manipulation for desired perovskite phase:PartⅠ.Paradigm demonstration in tungsten oxides

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Journal of Materiomics 2024年第2期10卷 293-303页

作者： Cheng Fang Hong Wang Siqi Shi State key laboratory of Green Building Materials China Building Materials AcademyBeijing100024China Beijing key laboratory of Solar Energy and Building Energy-saving Glass Materials processing Technology China Building Materials AcademyBeijing100024China Materials Genome Initiative Center Shanghai Jiao Tong UniversityShanghai200240China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China School of Materials Science and Engineering Shanghai UniversityShanghai200444China Materials Genome Institute Shanghai UniversityShanghai200444China

Slight distortions can cause dramatic changes in the properties of crystalline perovskite materials and their *** to the numerous types of distortions and unclarified distortion-structure relations,a quantitative distortion manipulation for the desired crystalline phase of perovskite materials suitable for various application remains ***,by establishing parameter sets to systematically describe the types,magnitudes and positional relations involved in distortions,we are able to interpret the structural regulations and manipulation strategies in 7 reported crystal *** the con-struction of distortion-phase-property functional curves,we further propose a paradigm to quantify the structural distortion manipulation for desired perovskite *** the example of perovskite-like tungsten oxides,we successfully quantify their volume shrinkage and symmetry increase during *** work verifies that the complicated research and development of perovskite materials can be simplified into a mathematical problem solving process,which will inspire researchers with different backgrounds to participate,especially mathematicians and computer scientists.

关键词： Distortion manipulation Distortion-phase-property function Coordination polyhedron Tungsten oxide Perovskite

Research on Improved MobileViT image Tamper Localization Model

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computers, Materials & Continua 2024年第8期80卷 3173-3192页

作者： Jingtao Sun Fengling Zhang Huanqi Liu Wenyan Hou School of computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710121China Shaanxi key laboratory of Network Data Analysis and Intelligent processing Xi’an University of Posts and TelecommunicationsXi’an710121China

As image manipulation technology advances rapidly,the malicious use of image tampering has alarmingly escalated,posing a significant threat to social *** the realm of image tampering localization,accurately localizing limited samples,multiple types,and various sizes of regions remains a multitude of *** issues impede the model’s universality and generalization capability and detrimentally affect its *** tackle these issues,we propose FL-MobileViT-an improved MobileViT model devised for image tampering *** proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain,and captures richer traces of tampering through dual-stream ***,the model incorporating the Focused Linear Attention mechanism within the lightweight network(MobileViT).This substitution significantly diminishes computational complexity and resolves homogeneity problems associated with traditional Transformer attention mechanisms,enhancing feature extraction diversity and improving the model’s localization *** comprehensively fuse the generated results from both feature extractors,we introduce the ASPP architecture for multi-scale feature *** facilitates a more precise localization of tampered regions of various ***,to bolster the model’s generalization ability,we adopt a contrastive learning method and devise a joint optimization training strategy that leverages fused features and captures the disparities in feature distribution in tampered *** strategy enables the learning of contrastive loss at various stages of the feature extractor and employs it as an additional constraint condition in conjunction with cross-entropy *** a result,overfitting issues are effectively alleviated,and the differentiation between tampered and untampered regions is *** evaluations on five benchmark datasets(IMD-20,CASIA,NIST-16,Columbia and Coverage)validat

关键词： image tampering localization focused linear attention mechanism MobileViT contrastive loss

Highly efficient solution-processed organic photovoltaics enabled by improving packing behavior of organic semiconductors

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Science China Chemistry 2024年第3期67卷 890-897页

作者： Xinyue Cui Hongxiang Li Hao Lu Yuqiang Liu Guangliu Ran Rui Liu Huarui Zhang Xueqing Ma Dawei Li Yi Lin Jifa Yu Wenkai Zhang Lei Cai Yahui Liu Pei Cheng Andong Zhang Zaifei Ma Guanghao Lu Zhishan Bo College of Textiles and Clothing State Key Laboratory of Bio-fibers and Eco-textilesQingdao UniversityQingdao 266071China State key laboratory of Polymer Materials Engineering College of Polymer Science and EngineeringSichuan UniversityChengdu 610065China Department of Physics and Applied Optics Beijing Area Major laboratory Center for Advanced Quantum StudiesBeijing Normal UniversityBeijing 100875China Shandong Province key laboratory of medical Physics and image processing Technology School of Physics and ElectronicsShandong Normal UniversityJinan 250014China Beijing key laboratory of Energy Conversion and Storage Materials College of ChemistryBeijing Normal UniversityBeijing 100875China Center for Advanced Low-Dimension Materials State Key Laboratory for Modification of Chemical Fibers and Polymer MaterialsCollege of Materials Science and EngineeringDonghua UniversityShanghai 201620China Frontier Institute of Science and Technology Xi'an Jiaotong UniversityXi'an 710054China

Solution processability is a unique property of organic semiconductors. The compact and regular π-π stacking between molecules is paramount in the performance of organic optoelectronic devices. However, it is still a challenge to improve their stacking quality without sacrificing the solution-processability from the aspect of materials design. Here, delicately engineered additives are presented to promote the formation of ordered aggregation of conjugated molecules by regulating their nucleation and growth dynamics. Intriguingly, the long-chain BTP-eC9-4F molecules can realize ordered aggregation comparable to short-chain ones without sacrificing processability. The domain size of BTP-eC9-4F aggregation is enlarged from 24.2 to 32.2 nm in blend *** exciton diffusion and charge transport become faster, contributing to the suppression of recombination losses. As a result, a power conversion efficiency of 19.2% is achieved in D18:BTP-eC9-4F based organic photovoltaics. Our findings demonstrate a facile strategy to improve the packing quality of solution-processed organic semiconductors for high-efficiency photovoltaics and beyond photovoltaics.

关键词： organic semiconductor organic photovoltaic charge transport solution processability

Multi-Channel Based on Attention Network for Infrared Small Target Detection

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Journal of shanghai Jiaotong university(Science) 2024年第3期29卷 414-427页

作者：张彦军王碧云蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China shanghai Institute of Aerospace Control Technology Shanghai 201109China Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation Shanghai 201109China key laboratory of System Control and Information processing Ministry of Education of ChinaShanghai 200240China shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China key laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking ***,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning ***,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target ***,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target *** addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion ***,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.

关键词： infrared image small target detection deep learning attention mechanism feature fusion

When debugging encounters artificial intelligence: state of the art and open challenges

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Science China(Information Sciences) 2024年第4期67卷 42-84页

作者： Yi SONG Xiaoyuan XIE Baowen XU School of computer Science Wuhan University State key laboratory of Novel Software Technology Nanjing University

Both software debugging and artificial intelligence techniques are hot topics in the current field of software engineering. Debugging techniques, which comprise fault localization and program repair, are an important part of the software development lifecycle for ensuring the quality of software systems. As the scale and complexity of software systems grow, developers intend to improve the effectiveness and efficiency of software debugging via artificial intelligence(artificial intelligence for software debugging, AI4SD). On the other hand, many artificial intelligence models are being integrated into safety-critical areas such as autonomous driving, image recognition, and audio processing, where software debugging is highly necessary and urgent(software debugging for artificial intelligence, SD4AI). An AI-enhanced debugging technique could assist in debugging AI systems more effectively, and a more robust and reliable AI approach could further guarantee and support debugging techniques. Therefore, it is important to take AI4SD and SD4AI into consideration comprehensively. In this paper, we want to show readers the path, the trend, and the potential that these two directions interact with each other. We select and review a total of 165 papers in AI4SD and SD4AI for answering three research questions, and further analyze opportunities and challenges as well as suggest future directions of this cross-cutting area.

关键词： software debugging fault localization program repair artificial intelligence machine learning

Brain-inspired artificial intelligence research: A review

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Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing key laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China key laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of computer and Information Science Chongqing Normal UniversityChongqing 401331China State key laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State key laboratory of Multimedia Information processing School of Computer SciencePeking UniversityBeijing 100871China Department of computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model