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Towards AI-Powered Edge intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

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FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models 38

FIRE: A Dataset for Feedback Integration and Refinement Eval...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Pengxiang Gao, Zhi Zhang, Bofei Yuan, Tao Wu, Yuwei Harandi, Mehrtash Jia, Yunde Zhu, Song-Chun Li, Qing Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China State Key Laboratory of General Artificial Intelligence BIGAI China State Key Laboratory of General Artificial Intelligence Peking University China Department of Electrical and Computer System Engineering Monash University Australia Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Automation Tsinghua University China

vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset. © 2024 Neural information processing systems foundation. All rights reserved.

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Interpretable Drug Response Prediction through Molecule Structure-aware and Knowledge-Guided Visible Neural Network

Interpretable Drug Response Prediction through Molecule Stru...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xie, Jiancong Zhang, Zhe Li, Youyou Rao, Jiahua Yang, Yuedong Sun Yat-sen University School of Computer Science and Engineering China University of Chicago Department of Chemistry IL United States Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing of Moe China

ISBN: (纸本)9798350386226

Precise prediction of anti-cancer drug responses has become a crucial obstruction in anti-cancer drug design and clinical applications. In recent years, various deep learning methods have been applied to drug response prediction and become more accurate. However, they are still criticized as being non-transparent. To offer reliable drug response prediction in real-world applications, there is still a pressing demand to develop a model with high predictive performance as well as interpretability. In this study, we propose DrugVNN, an end-to-end interpretable drug response prediction framework, which extracts gene features of cell lines through a knowledge-guided visible neural network (VNN) and learns drug representation through a node-edge communicative message passing network (CMPNN). Additionally, between these two networks, a novel drug-aware gene attention gate is designed to direct the drug representation to VNN to simulate the effects of drugs. By evaluating on the GDSC dataset, DrugVNN achieved state-of-the-art performance. Moreover, DrugVNN can identify active genes and relevant signaling pathways for specific drug-cell line pairs with supporting evidence in the literature, implying the interpretability of our model. © 2024 IEEE.

关键词： Prediction models

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Understanding adversarial attacks on observations in deep reinforcement learning

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science China(Information sciences) 2024年第5期67卷 69-83页

作者： You QIAOBEN Chengyang YING Xinning ZHOU Hang SU Jun ZHU Bo ZHANG Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua-Bosch Joint Center for Machine Learning Institute for Artificial Intelligence Tsinghua University Peng Cheng Laboratory

Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease the cumulative expected reward of a victim by manipulating its observations. Despite the efficiency of previous optimization-based methods for generating adversarial noise in supervised learning, such methods might not achieve the lowest cumulative reward since they do not generally explore the environmental ***, a framework is provided to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space. The reformulation approach adopted herein generates an optimal adversary in the function space of targeted attacks, repelling them via a generic two-stage framework. In the first stage, a deceptive policy is trained by hacking the environment and discovering a set of trajectories routing to the lowest reward or the worst-case performance. Next, the adversary misleads the victim to imitate the deceptive policy by perturbing the observations. Compared to existing approaches, it is theoretically shown that our adversary is strong under an appropriate noise level. Extensive experiments demonstrate the superiority of the proposed method in terms of efficiency and effectiveness, achieving state-of-the-art performance in both Atari and MuJoCo environments.

关键词： deep learning reinforcement learning adversarial robustness adversarial attack

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Learning Multiresolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2024年第1期5卷 127-139页

作者： Tao, Xian Yan, Shaohua Gong, Xinyi Adak, Chandranath Institute of Automation Chinese Academy of Sciences Beijing100190 China Binzhou Institute of Technology Binzhou256606 China CAS Engineering Laboratory for Intelligent Industrial Vision Beijing100190 China School of Artificial Intelligence University of ChineseAcademy of Sciences Beijing100049 China Department of Computer Science and Engineering Indian Institute of Technology Bihar Patna801106 India

In industrial quality assessment, monitoring whether the textured product contains defects is a critical step. Compared to a large number of defect-free images that are easy to obtain, anomaly samples are limited and vary randomly in size and type. It is challenging to develop an automatic and accurate texture defect localization system that only uses normal images for training. In this article, a multiresolution feature learning network is proposed to detect various texture defects in an unsupervised manner. A robust pretrained model is first employed to extract the perceptual features from the input image, then the perceptual features of various layers are fed to the corresponding multiscale autoencoder framework. This hierarchical alignment strategy aids in receiving multilevel information for locating anomalies of various sizes. Moreover, a residual attention module is embedded in the architecture to further improve the detection performance. Our proposed method has achieved state-of-the-art performance on the texture dataset of MVTecAD. We also extended the experiment to the real industrial texture datasets, and its detection result is better than the major existing advanced techniques. © 2020 IEEE.

关键词： Textures

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DMHFR:Decoder with Multi-Head Feature Receptors for Tract Image Segmentation

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computers, Materials & Continua 2025年第3期82卷 4841-4862页

作者： Jianuo Huang Bohan Lai Weiye Qiu Caixu Xu Jie He Department of Endoscopy Center Zhongshan Hospital(Xiamen)Fudan UniversityXiamen361015China School of Computing and Data Science Xiamen University MalaysiaSepang43900Malaysia School of Computer Science and Techonology Tongji UniversityShanghai200092China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou UniversityWuzhou543002China Xiamen Clinical Research Center for Cancer Therapy Xiamen361015China

The self-attention mechanism of Transformers,which captures long-range contextual information,has demonstrated significant potential in image ***,their ability to learn local,contextual relationships between pixels requires further *** methods face challenges in efficiently managing multi-scale fea-tures of different granularities from the encoder backbone,leaving room for improvement in their global representation and feature extraction *** address these challenges,we propose a novel Decoder with Multi-Head Feature Receptors(DMHFR),which receives multi-scale features from the encoder backbone and organizes them into three feature groups with different granularities:coarse,fine-grained,and full *** groups are subsequently processed by Multi-Head Feature Receptors(MHFRs)after feature capture and modeling *** include two Three-Head Feature Receptors(THFRs)and one Four-Head Feature Receptor(FHFR).Each group of features is passed through these MHFRs and then fed into axial transformers,which help the model capture long-range dependencies within the *** three MHFRs produce three distinct feature *** output from the FHFR serves as auxiliary auxiliary features in the prediction head,and the prediction output and their losses will eventually be *** results show that the Transformer using DMHFR outperforms 15 state of the arts(SOTA)methods on five public ***,it achieved significant improvements in mean DICE scores over the classic Parallel Reverse Attention Network(PraNet)method,with gains of 4.1%,2.2%,1.4%,8.9%,and 16.3%on the CVC-ClinicDB,Kvasir-SEG,CVC-T,CVC-ColonDB,and ETIS-LaribPolypDB datasets,respectively.

关键词： Medical image segmentation feature exploration feature aggregation deep learning multi-head feature receptor

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LLaVA-Endo:a large language-and-vision assistant for gastrointestinal endoscopy

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Frontiers of computer science 2025年第4期19卷 121-123页

作者： Jieru YAO Xueran LI Qiang XIE Longfei HAN Yiwen JIA Nian LIU Dingwen ZHANG Junwei HAN School of Automation Northwestern Polytechnical UniversityXi’an 710072China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China AHU-IAI AI Joint Laboratory Anhui UniversityHefei 230039China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230026China School of Computer and Artificial Intelligence Beijing Technology and Business UniversityBeijing 100048China Department of Gastroenterology The Third Affiliated Hospital of Anhui Medical University(Hefei First People’s Hospital)Hefei 230061China The Computer Vision Department Mohamed Bin Zayed University of Artificial IntelligenceMasdarAbu Dhabi 200120United Arab Emirates Xijing Hospital The Fourth Military Medical UniversityXi’an 710032China

1 Introduction Endoscopy plays a crucial role in the diagnoses and treatment of gastrointestinal(GI)diseases[1],as it helps to identify abnormalities,classify lesion,and determine treatment *** GI endoscopic examinations,physicians may encounter practical hindrances,i.e.,fatigue,stress,or limited experience,which can lead to erroneous *** intelligence(AI)-assisted GI endoscopy technology has emerged to address these limitations[2].

关键词： stress giendoscopy fatigue identify abnormalitiesclassify lesionidentification artificialintelligence

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Graph Convolutional Network Combined with Semantic Feature Guidance for Deep Clustering

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Tsinghua science and Technology 2022年第5期27卷 855-868页

作者： Junfen Chen Jie Han Xiangjie Meng Yan Li Haifeng Li Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province the College of Mathematics and Information ScienceHebei UniversityBaoding 071002China School of Applied Mathematics Beijing Normal University ZhuhaiZhuhai 519087China Department of Computer Teaching Hebei UniversityBaoding 071002China

The performances of semisupervised clustering for unlabeled data are often superior to those of unsupervised learning,which indicates that semantic information attached to clusters can significantly improve feature representation *** a graph convolutional network(GCN),each node contains information about itself and its neighbors that is beneficial to common and unique features among *** these findings,we propose a deep clustering method based on GCN and semantic feature guidance(GFDC) in which a deep convolutional network is used as a feature generator,and a GCN with a softmax layer performs clustering ***,the diversity and amount of input information are enhanced to generate highly useful representations for downstream ***,the topological graph is constructed to express the spatial relationship of *** a pair of datasets,feature correspondence constraints are used to regularize clustering loss,and clustering outputs are iteratively *** external evaluation indicators,i.e.,clustering accuracy,normalized mutual information,and the adjusted Rand index,and an internal indicator,i.e., the Davidson-Bouldin index(DBI),are employed to evaluate clustering *** results on eight public datasets show that the GFDC algorithm is significantly better than the majority of competitive clustering methods,i.e.,its clustering accuracy is20% higher than the best clustering method on the United States Postal Service *** GFDC algorithm also has the highest accuracy on the smaller Amazon and Caltech ***,DBI indicates the dispersion of cluster distribution and compactness within the cluster.

关键词： self-supervised clustering graph convolutional network feature correspondence semantic feature guidance confusion matrix evaluation indicator

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Tile selection method based on error minimization for photomosaic image creation

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Frontiers of computer science 2021年第3期15卷 165-172页

作者： Hongbo ZHANG Xin GAO Jixiang DU Qing LEI Lijie YANG Department of Computer Science and Technology Huaqiao UniversityXiamen 361021China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao UniversityXiamen 361021China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao UniversityXiamen 361021China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055China

Photomosaic images are composite images composed of many small images called *** its overall visual effect,a photomosaic image is similar to the target image,and photomosaics are also called“montage art”.Noisy blocks and the loss of local information are the major obstacles in most methods or programs that create photomosaic *** solve these problems and generate a photomosaic image in this study,we propose a tile selection method based on error minimization.A photomosaic image can be generated by partitioning the target image in a rectangular pattern,selecting appropriate tile images,and then adding them with a weight *** on the principles of montage art,the quality of the generated photomosaic image can be evaluated by both global and local *** the proposed framework,via an error function analysis,the results show that selecting a tile image using a global minimum distance minimizes both the global error and the local error ***,the weight coefficient of the image superposition can be used to adjust the ratio of the global and local ***,to verify the proposed method,we built a new photomosaic creation dataset during this *** experimental results show that the proposed method achieves a low mean absolute error and that the generated photomosaic images have a more artistic effect than do the existing approaches.

关键词： photomosaic image tile image target image error minimization mean absolute error

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An Automatic Lung Nodule Classification System Based on Hybrid Transfer Learning Approach

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SN computer science 2022年第4期3卷 1-10页

作者： Saikia, Trishna Kumar, Ranjeet Kumar, Dilip Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jamshedpur 831014 India School of Electronics Engineering Vellore Institute of Technology Chennai Campus Chennai 600127 India

Lung cancers are not only affecting the lung but also impair the respiratory mechanism. Worldwide, it becomes one of the high causes of mortality in humans. Several computer aided diagnosis (CAD) systems have been designed in recent years for diagnosis of several diseases. Early detection of lung cancer has become very important which can enhance survival chances among humans. The average rates of survival people with lung-cancer can rise from 14 to 49 percent if the diagnosed is taking place in time. Compare to X-ray, computed tomography (CT) is more operative as it involves multiple imaging approaches to support each other. In this study, we have used lung-patient CT scan images to classify the lung nodule into four classes, which are small-cell-carcinoma, adenocarcinoma, squamous-cell-carcinoma and large-cell-carcinoma. This study has proposed VGG networks integrated with support vector machine and random forest to create a hybrid algorithm that further helps in reducing computation complexity of classification. The lung nodules are classified using these hybrids algorithms with highest accuracy 98.70% and compared the results with existing methods. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Computed tomography Convolution neural network Random forest Support vector machine

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