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InstaCropNet:An efficient Unet-Based architecture for precise crop row detection in agricultural applications

Artificial Intelligence in Agriculture 2024年第2期12卷 85-96页

作者： Zhiming Guo Yuhang Geng Chuan Wang Yi Xue Deng Sun Zhaoxia Lou Tianbao Chen Tianyu Geng Longzhe Quan School of Engineering Anhui Agricultural UniversityHefei 230036China College of Engineering Northeast Agricultural UniversityHarbin 150030China School of Mathematics and Computer Science Nanchang UniversityNanchang 330031China

Autonomous navigation in farmlands is one of the key technologies for achieving autonomous management in maize *** various navigation techniques,visual navigation using widely available RGB images is a cost-effective ***,current mainstream methods for maize crop row detection often rely on highly specialized,manually devised heuristic rules,limiting the scalability of these *** simplify the solution and enhance its universality,we propose an innovative crop row annotation *** strategy,by simulating the strip-like structure of the crop row's central area,effectively avoids interference from lateral growth of crop *** on this,we developed a deep learning network with a dual-branch architecture,InstaCropNet,which achieves end-to-end segmentation of crop row ***,through the row anchor segmen-tation technique,we accurately locate the positions of different crop row instances and perform line *** results demonstrate that our method has an average angular deviation of no more than 2°,and the accuracy of crop row detection reaches 96.5%.

关键词： Visual navigation Deep learning Maize crop row detection Early corn crops Row clustering Crop row segmentation

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Toward Secure Trajectory Similarity Range Query Under Multiuser Setting

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IEEE Internet of Things Journal 2025年第11期12卷 16336-16348页

作者： Cui, Ningning Sun, Xun Pei, Lili Wang, Mengxiang Wang, Dong Li, Jianxin Jin, Hulin Cui, Jie Zhong, Hong Anhui University School of Computer Science and Technology Hefei230093 China Chang’an University School of Data Science and Artificial Intelligence Xi’an710064 China China National Institute of Standardization Beijing China Deakin University School of Information Technology GeelongVIC3220 Australia Anhui University School of Big Data Hefei China

The widespread availability of similarity queries over trajectory data has led to numerous real-world applications, such as traffic management and path planning. With the proliferation of trajectory data, data owners often outsource storage and computation tasks to the cloud due to limited computing and storage resources. However, this scenario raises sharp security concerns, where it is critical to ensure both the integrity of query results and privacy during query processing. Furthermore, most existing works assume a single-user setting where all query users share the same key, which may lead to query privacy leakage. Therefore, in this article, we take the first step in studying the issue of multiuser and secure trajectory similarity range query (MSRQ). Specifically, inspired by the M-tree, we propose a secure index based on a distributed two-trapdoor public-key cryptosystem (DT-PKC), called M*-tree, and devise secure protocols to support multiuser query processing. We also carefully design a filtering strategy and verification scheme to ensure fast search and integrity guarantees. Finally, we theoretically analyze the security and complexity and empirically evaluate the performance and feasibility of our proposed approach. © 2014 IEEE.

关键词： Data integrity

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CBNet: Cooperation-Based Weakly Supervised Polyp Detection 24

CBNet: Cooperation-Based Weakly Supervised Polyp Detection

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32nd ACM International Conference on Multimedia, MM 2024

作者： Du, Xiuquan Chen, Jiajia Zhang, Xuejun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Anhui Hefei China School of Computer Science and Technology Anhui University Anhui Hefei China

ISBN: (纸本)9798400706868

Missed polyps are the major risk factor for colorectal cancer. To minimize misdiagnosis, many methods have been developed. However, they either rely on laborious instance-level annotations, require labeling of prompt points, or lack the ability to filter noise proposals and detect polyps integrally, resulting in severe challenges in this area. In this paper, we propose a novel Cooperation-Based network (CBNet), a two-stage polyp detection framework supervised by image labels that removes wrong proposals through classification in collaboration with segmentation and obtains a more accurate detector by aggregating adaptive multi-level regional features. Specifically, we conduct a Cooperation-Based Region Proposal Network (CBRPN) to reduce the negative impact of noises by deleting proposals without polyps, enabling our network to capture polyp features. Moreover, to enhance location integrity and classification precision of polyps, we aggregate multi-level region of interest (ROI) features under the guidance of the backbone classification layer, namely Adaptive ROI Fusion Module (ARFM). Extensive experiments on the public and private datasets show that our method achieves state-of-the-art performance for weakly supervised methods and even outperforms full supervision in some terms. All code is available at https://***/dxqllp/CBNet. © 2024 ACM.

关键词： Image segmentation

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Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1530-1543页

作者： Guijun Ma Zidong Wang Weibo Liu Jingzhong Fang Yong Zhang Han Ding Ye Yuan The School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China the Department of Computer Science Brunel University LondonUxbridgeMiddlesexUB83PHUnited Kingdom the School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan 430081China the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized *** this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution *** standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based *** proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge *** results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based *** Py Torch implementation of the proposed PSO-DDA method is available at https://***/mxt0607/PSO-DDA.

关键词： Deep transfer learning domain adaptation hyperparameter selection lithium-ion batteries(LIBs) particle swarm optimization state of health estimation(SOH)

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Polarization dehazing method of UAV aerial image based on haze density estimation 4

Polarization dehazing method of UAV aerial image based on ha...

引用

4th International Conference on Signal Image Processing and Communication, ICSIPC 2024

作者： Luo, Maowen Sun, Chao School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9781510682467

The distribution of haze in UAV aerial haze images is usually inhomogeneous, and the traditional dehazing algorithms that may have some problems such as haze residue or excessive dehazing when processing them, this paper brings up a polarization dehazing method based on the calculation of haze density. Firstly, the minimum RGB color channel map is calculated to establish the haze density distribution map, and the dynamic scattering coefficient is constructed on the basis of the haze density and the transmittance is estimated. Secondly, the local atmospheric polarization degree is automatically estimated by combining the polarization degree and the transmittance, so as to realize the accurate separation of the atmospheric scattered light. Then, a two-scale correction method based on the detection of highlight regions is applied with the transmission for the image which contains highlight regions. Finally, the clear image is acquired by transforming atmospheric scattering model. The results of experiments show that our method can effectively remove the haze in the UAV aerial haze images, and has achieved excellent performance in both subjective evaluation and objective index. © 2024 SPIE.

关键词： Demulsification

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Digital twin empowered cooperative trajectory planning of platoon vehicles for collision avoidance with unexpected obstacles

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Digital Communications and Networks 2024年第6期10卷 1666-1676页

作者： Hao Du Supeng Leng Jianhua He Kai Xiong Longyu Zhou School of Information&Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUnited Kingdom Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China

Road obstacles that unexpectedly appear due to vehicle breakdowns and accidents are major causes of fatal road *** Autonomous Vehicles(CAVs)can be used to avoid collisions to ensure road safety through cooperative sensing and ***,the collision avoidance performance of CAVs with unexpected obstacles has not been studied in the existing *** this paper,we first design a platoon-based collision avoidance framework for *** this framework,we deploy a Digital Twin(DT)system at the head vehicle in a platoon to reduce communication overhead and decision-making delay based on a proposed trajectory planning *** addition,a DT-assistant system is deployed on the assistant vehicle to monitor vehicles out of the sensing range of the head vehicle for the maintenance of the DT *** this case,the transmission frequency of kinetic states of platoon members can be reduced to ensure low-overhead ***,we design a variable resource reservation interval that can ensure DT synchronization between DT and the assistant system with high *** further improve road safety,an urgency level-based trajectory planning algorithm is proposed to avoid unexpected obstacles considering different levels of emergency *** results show that our DT system-based scheme can achieve significant performance gains in unexpected obstacle *** to the existing schemes,it can reduce collisions by 95%and is faster by about 10%passing by the unexpected obstacle.

关键词： Platoon driving Cooperative trajectory planning Digital twin Obstacle avoidance

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TriCvT-DTI: Predicting Drug-Target Interactions Using Trimodal Representations and Convolutional Vision Transformers

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4585-4592页

作者： Maroua, Azouz Tian, Gang Wang, Rui Zhou, Jiehan Shandong University of Science and Technology Qingdao266510 China Shandong University of Science and Technology School of Computer Science and Engineering Qingdao266510 China Shandong University of Science and Technology College of Energy and Mining Engineering Qingdao266510 China Shandong University of Science and Technology Qingdao China Docent at University of Oulu Oulu Finland

Predicting interactions between drugs and their targets is vital for drug discovery and repositioning. Conventional techniques are slow and labor-intensive, while deep learning algorithms offer efficient solutions. However, deep learning often focus on single drug representations or simplistic combinations, leading to suboptimal feature representation. Moreover, the prevalent use of convolutional neural networks (CNNs) in drug image representation neglects the necessity for both local and global drug information in Drug-Target Interaction (DTI) tasks. To address these challenges, we propose TriCvT-DTI, a novel approach that combines molecular images, chemical sequence features, and graph representations of drugs to comprehensively capture structural, spatial, and functional aspects. TriCvT-DTI introduces a bidirectional multi-head attention mechanism for interactive feature learning between drugs and targets, enhancing performance by modeling complex relationships. By using Convolutional Vision Transformers (CvTs), TriCvT-DTI can effectively extract structural and spatial features from drug images. We evaluate our model on three datasets: Human, C. elegans, and Davis, and we compare it with state-of-the-art methods. Then we train TriCvT-DTI with uni-modality and bi-modality to compare then extract the impact of each modality on TriCvT-DTI. Experimental results demonstrate that TriCvT-DTI outperforms existing methods on both balanced and unbalanced datasets. Moreover, it presents impressive generalization capabilities on the Drug-Target Interaction (DTI) task. © 2013 IEEE.

关键词： Molecular imaging

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Rank-Based singular value decomposition recommendation algorithm for academic web pages

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International Journal of computers and Applications 2025年第5期47卷 413-423页

作者： Naseer, Mehwish Zhang, Wu National University of Sciences and Technology Islamabad Pakistan School of Computer Engineering and Science Shanghai University Shanghai China

Finding appropriate information on the web is a tedious task and thus demands an intelligent mechanism to assist users for this purpose. Students are the victims of information overloading on the internet the most, as it hinders their learning. This study proposes a rank-based singular value decomposition (R-SVD) algorithm for making academic web page recommendations. The weblog of the network is a depiction of student's browsing habits. The proposed approach takes weblog data as input. The rank of the pages is calculated based on the hit ratio of each page. Rank values are normalized to get a scaled value of each page rank. Then R-SVD is used with multiple similarities and recommendation results are evaluated based on the different parameters such as by computation of root mean square error (RMSE), mean absolute error (MAE) and mean percent error (MPE). The proposed approach recommends web pages with the least error values and verdict that the proposed method is a justifiable technique for the academic web page recommendation system. The proposed approach is compared with multiple recommendation algorithms such as User-KNN, Item-KNN, Non-Negative Matrix Factorization (NMF), Bayesian User Community Model (BUCM), and Probabilistic Matrix Factorization (PMF) using numerous similarity measures. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Non-negative matrix factorization

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Local Fine-grained Visual Tracking

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IEEE Transactions on Multimedia 2025年 27卷 3426-3436页

作者： Wu, Jingjing Sun, Yifan Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Baidu China

This paper introduces a novel local fine-grained visual tracking task, aiming to precisely locate arbitrary local parts of objects. This task is motivated by our observation that in many realistic scenarios, the user demands to track a local part instead of a holistic object. However, the absence of an evaluation dataset and the distinctive characteristics of local fine-grained targets present extra challenges in conducting this research. To tackle these issues, firstly, this paper constructs a local fine-grained tracking (LFT) dataset to evaluate the tracking performance for local fine-grained targets. Secondly, this paper designs a cutting-edge solution to handle the challenges posed by properties of local objects, including ambiguity and high-proportion backgrounds. It consists of a hierarchical adaptive mask mechanism and foreground-background differentiated learning. The former adaptively searches for and masks ambiguity, which drives the network to concentrate on the local target instead of the holistic objects. The latter is constructed to distinguish foreground and background in an unsupervised manner, which is beneficial to mitigate the impacts of high-proportion backgrounds. Extensive analytic experiments are performed to verify the effectiveness of each submodule in the proposed fine-grained tracker. Our datasets and code will be publicly available soon: https://***/hfutwujingjing/local-fine-grained-tracking. © 1999-2012 IEEE.

关键词： Coarse-grained modeling

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A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

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