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AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes

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arXiv 2025年

作者： Li, Zhenteng Lian, Sheng Pan, Dengfeng Wang, Youlin Liu, Wei College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350002 China School of Software East China Jiaotong University Nanchang330013 China

Object detection in Unmanned Aerial Vehicle (UAV) images poses significant challenges due to complex scale variations and class imbalance among objects. Existing methods often address these challenges separately, overlooking the intricate nature of UAV images and the potential synergy between them. In response, this paper proposes AD-Det, a novel framework employing a coherent coarse-to-fine strategy that seamlessly integrates two pivotal components: Adaptive Small Object Enhancement (ASOE) and Dynamic Class-balanced Copy-paste (DCC). ASOE utilizes a high-resolution feature map to identify and cluster regions containing small objects. These regions are subsequently enlarged and processed by a fine-grained detector. On the other hand, DCC conducts object-level resampling by dynamically pasting tail classes around the cluster centers obtained by ASOE, maintaining a dynamic memory bank for each tail class. This approach enables AD-Det to not only extract regions with small objects for precise detection but also dynamically perform reasonable resampling for tail-class objects. Consequently, AD-Det enhances the overall detection performance by addressing the challenges of scale variations and class imbalance in UAV images through a synergistic and adaptive framework. We extensively evaluate our approach on two public datasets, i.e., VisDrone and UAVDT, and demonstrate that AD-Det significantly outperforms existing competitive alternatives. Notably, AD-Det achieves a 37.5% Average Precision (AP) on the VisDrone dataset, surpassing its counterparts by at least 3.1%. Code will be available at https://***/gentlezachary/AD-Det. Copyright © 2025, The Authors. All rights reserved.

关键词： Vehicle detection

Ransomware Classification and Detection With Machine Learning Algorithms

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arXiv 2022年

作者： Masum, Mohammad Faruk, Md Jobair Hossain Shahriar, Hossain Qian, Kai Lo, Dan Adnan, Muhaiminul Islam School of Data Science Kennesaw State University United States Department of Software Engineering and Game Development Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department Computer Science Kennesaw State University United States Institute of Natural Sciences United International University Bangladesh

Malicious attacks, malware, and ransomware families pose critical security issues to cybersecurity, and it may cause catastrophic damages to computer systems, data centers, web, and mobile applications across various industries and businesses. Traditional anti-ransomware systems struggle to fight against newly created sophisticated attacks. Therefore, state-of-the-art techniques like traditional and neural network-based architectures can be immensely utilized in the development of innovative ransomware solutions. In this paper, we present a feature selection-based framework with adopting different machine learning algorithms including neural network-based architectures to classify the security level for ransomware detection and prevention. We applied multiple machine learning algorithms: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR) as well as Neural Network (NN)-based classifiers on a selected number of features for ransomware classification. We performed all the experiments on one ransomware dataset to evaluate our proposed framework. The experimental results demonstrate that RF classifiers outperform other methods in terms of accuracy, F-beta, and precision scores. © 2022, CC BY.

关键词： Malware

A novel MDPSO-SVR hybrid model for feature selection in electricity consumption forecasting

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arXiv 2022年

作者： Bao, Yukun Shen, Liang Zhang, Xiaoyuan Huang, Yanmei Deng, Changrui Center for Modern Information Management School of Management Huazhong University of Science and Technology Wuhan430074 China Department of Public Curriculum Studies Jiangxi University of Software Professional Technology Nanchang330041 China Center of Big Data Analytics Jiangxi University of Engineering Xinyu338029 China

Electricity consumption forecasting has vital importance for the energy planning of a country. Of the enabling machine learning models, support vector regression (SVR) has been widely used to set up forecasting models due to its superior generalization for unseen data. However, one key procedure for the predictive modeling is feature selection, which might hurt the prediction accuracy if improper features were selected. In this regard, a modified discrete particle swarm optimization (MDPSO) was employed for feature selection in this study, and then MDPSO-SVR hybrid model was built to predict future electricity consumption. Compared with other well-established counterparts, MDPSO-SVR model consistently performs best in two real-world electricity consumption datasets, which indicates that MDPSO for feature selection can improve the prediction accuracy and the SVR equipped with the MDPSO can be a promised alternative for electricity consumption forecasting. © 2022, CC BY.

关键词： Feature Selection

NuSegDG: Integration of heterogeneous space and Gaussian kernel for domain-generalized nuclei segmentation

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Knowledge-Based Systems 2025年 322卷

作者： Lou, Zhenye Xu, Qing Jiang, Zekun He, Xiangjian Li, Chenxin Chen, Zhen Wang, Yi He, Maggie M. Duan, Wenting Sichuan University Pittsburgh Institute Sichuan University Chengdu China School of Computer Science Computer Vision and Intellifent Perception Laboratory and Municipal Digital Port Technology Key Laboratory University of Nottingham Ningbo Zhejiang China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong School of Software Dalian University of Technology Dalian116600 China Department of Electronic Engineering The Chinese University of Hong Kong 999077 Hong Kong Department of Cardiology Gold Coast University Hospital QLD Australia School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom

Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image conditions, cell types, and stain strategies. Recently, the Segment Anything Model (SAM) has made great success in universal image segmentation by interactive prompt modes (e.g., point and box). Despite its strengths, the original SAM presents limited adaptation to medical images. Moreover, SAM requires providing manual bounding box prompts for each object to produce satisfactory segmentation masks, so it is laborious in nuclei segmentation scenarios. To address these limitations, we propose a domain-generalizable framework for nuclei image segmentation, abbreviated to NuSegDG. Specifically, we first devise a Heterogeneous Space Adapter (HS-Adapter) to learn multi-dimensional feature representations of different nuclei domains by injecting a small number of trainable parameters into the image encoder of SAM. To alleviate the labour-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts. Furthermore, we present a Two-Stage Mask Decoder (TSM-Decoder) to effectively convert semantic masks to instance maps without the manual demand for morphological shape refinement. Based on our experimental evaluations, the proposed NuSegDG demonstrates state-of-the-art performance in nuclei semantic and instance segmentation, exhibiting superior domain generalization capabilities. The source code is available at https://***/xq141839/NuSegDG. © 2025 Elsevier B.V.

关键词： Semantic Segmentation

Study of facial generation methods after orthodontic treatment

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Study of facial generation methods after orthodontic treatme...

IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Jia– Liang Tian Qin– Yan Zhang Hai– Zhen Li Qing Wang Yi Lei Lin Zang Xue– Mei Gao Ji– Jiang Yang College of artificial intelligence Beijing University of Posts and Telecommunications Beijing Department of Orthodontics Peking University School and Hospital of Stomatology Beijing Department of Automation Tsinghua University Beijing Faculty of Information Technology School of Software Engineering Beijing University of Technology Beijing Pharmacovigilance Research Center for information technology and Data Science Xiamen Fujian

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

As the medical aesthetic market is growing rapidly in China, orthodontic treatment is becoming very common among the adolescent population. However, there are countless doctor-patient disputes due to treatment results that do not meet patients' expectations, so there is an urgent need for a method to predict treatment results. With the development of artificial intelligence technology, generative adversarial network has provided us with a new way of thinking. The purpose of this paper is to accurately predict the face of patients after orthodontic treatment by using generative adversarial network. Therefore, we designed an evaluation index to reflect the difference between the algorithm predicted image and the patient's real image. After that, we designed a network based on Encoder-Decoder architecture to transform the vectors in StyleGAN latent space. Finally, we carried out experiments to verify the effectiveness of the evaluation index design and the advantages of the algorithm.

关键词： Measurement Technological innovation Medical services Transforms Predictive models Prediction algorithms Generative adversarial networks

DVS: Deep Visibility Series and its Application in Construction Cost Index Forecasting

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arXiv 2021年

作者： Zhan, Tianxiang He, Yuanpeng Li, Hanwen Xiao, Fuyuan College of Computer and Information Science College of Software Southwest University Chongqing400715 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China National Eng. Lab. for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

Time series forecasting is a hot spot in recent years. Visibility Graph (VG) algorithm is used for time series forecasting in previous research, but the forecasting effect is not as good as deep learning prediction methods such as methods based on Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). The visibility graph generated from specific time series contains abundant network information, but the previous forecasting method did not effectively use the network information to forecast, resulting in relatively large prediction errors. To optimize the forecasting method based on VG, this article proposes the Deep Visibility Series (DVS) module through the bionic design of VG and the expansion of the past research. By applying the bionic design of biological vision to VG, DVS has obtained superior forecasting accuracy. At the same time, this paper applies the DVS forecasting method to the construction cost index forecast, which has practical significance. Copyright © 2021, The Authors. All rights reserved.

关键词： Visibility

Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle

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arXiv 2022年

作者： Wu, Junde Fang, Huihui Xiong, Haoyi Duan, Lixin Tan, Mingkui Yang, Weihua Liu, Huiying Xu, Yanwu Baidu Inc. Beijing China School of Computer Science and Technology University of Electronic Science and Technology of China Sichuan Chengdu China School of Software Engineering South China University of Technology Guangdong Guangzhou China Big Data and Artificial Intelligence Institute Shenzhen Eye Hospital Jinan University Guangdong Shenzhen China Institute for Infocomm Research A*STAR Singapore Singapore

On the medical images, many of the tissues/lesions may be ambiguous. That is why the medical segmentation is typically annotated by a group of clinical experts to mitigate the personal bias. However, this clinical routine also brings new challenges to the application of machine learning algorithms. Without a definite ground-truth, it will be difficult to train and evaluate the deep learning models. When the annotations are collected from different graders, a common choice is majority vote, e.g., taking the average of multiple labels. However such a strategy ignores the difference between the grader expertness. In this paper, we consider the task of predicting the segmentation with the calibrated inter-observer uncertainty. We note that in clinical practice, the medical image segmentation is usually used to assist the disease diagnosis. Inspired by this observation, we propose diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty. Following this idea, a framework named Diagnosis First segmentation Framework (DiFF) is proposed to estimate diagnosis-first segmentation from the raw images. Specifically, DiFF will first learn to fuse the multi-rater segmentation labels to a single ground-truth which could maximize the disease diagnosis performance. We dubbed the fused ground-truth as Diagnosis First Ground-truth (DF-GT). Then, we further propose Take and Give Model (T&G Model) to segment DF-GT from the raw image. In this way, DiFF can learn the segmentation with the calibrated uncertainty that facilitate the disease diagnosis. We verify the effectiveness of DiFF on three different medical segmentation tasks: optic-disc/optic-cup (OD/OC) segmentation on fundus images, thyroid nodule segmentation on ultrasound images, and skin lesion segmentation on dermoscopic images. Experimental results show that the proposed DiFF is able to significantly facilitate the corresponding disease diagnosis (gla

关键词： Diagnosis

AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning 16th

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AutoMix: Mixup Networks for Sample Interpolation via Coopera...

16th European Conference on Computer Vision, ECCV 2020

作者： Zhu, Jianchao Shi, Liangliang Yan, Junchi Zha, Hongyuan School of Software Engineering East China Normal University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9783030586065

This paper proposes new ways of sample mixing by thinking of the process as generation of barycenter in a metric space for data augmentation. First, we present an optimal-transport-based mixup technique to generate Wasserstein barycenter which works well on images with clean background and is empirically shown complementary to existing mixup methods. Then we generalize mixup to an AutoMix technique by using a learnable network to fit barycenter in a cooperative way between the classifier (a.k.a. discriminator) and generator networks. Experimental results on both multi-class and multi-label prediction tasks show the efficacy of our approach, which is also verified in the presence of unseen categories (open set) and noise. © 2020, Springer Nature Switzerland AG.

关键词： Image classification

Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism

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arXiv 2023年

作者： Yang, Han Wang, Qiuli Zhang, Yue An, Zhulin Chen, Liu Zhang, Xiaohong Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China The Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Radiology The First Affiliated Hospital of Army Medical University Chongqing400032 China School of Big Data & Software Engineering Chongqing University Chongqing401331 China Institute of Computing Technology Beijing100190 China

Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations. However, these approaches fail to leverage the valuable information inherent in the consensus and disagreements among the multiple annotations. In this paper, we propose an Uncertainty-Aware Attention Mechanism (UAAM) that utilizes consensus and disagreements among multiple annotations to facilitate better segmentation. To this end, we introduce the Multi-Confidence Mask (MCM), which combines a Low-Confidence (LC) Mask and a High-Confidence (HC) Mask. The LC mask indicates regions with low segmentation confidence, where radiologists may have different segmentation choices. Following UAAM, we further design an Uncertainty-Guide Multi-Confidence Segmentation Network (UGMCS-Net), which contains three modules: a Feature Extracting Module that captures a general feature of a lung nodule, an Uncertainty-Aware Module that produces three features for the annotations’ union, intersection, and annotation set, and an Intersection-Union Constraining Module that uses distances between the three features to balance the predictions of final segmentation and MCM. To comprehensively demonstrate the performance of our method, we propose a Complex Nodule Validation on LIDC-IDRI, which tests UGMCS-Net’s segmentation performance on lung nodules that are difficult to segment using common methods. Experimental results demonstrate that our method can significantly improve the segmentation performance on nodules that are difficult to segment using conventional methods. © 2023, CC BY-NC-ND.

关键词： Computerized tomography