Density-Based Detection Rapid Exploration Random Tree for Multirobot Formation Cooperative Path Planning by Yuzhuo Shi, Yang Yang, Jinjun Liu, Kun Hao, Jiale Zhao, Haoyi Chai

“…Second, the repulsion field function in the artificial potential field (APF) is optimized for local collaborative obstacle avoidance to enable multiple robots, and a rotational potential field is introduced to solve the problems of unreachable targets and local oscillations. …”
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A Spoofing Speech Detection Method Combining Multi-Scale Features and Cross-Layer Information by Hongyan Yuan, Linjuan Zhang, Baoning Niu, Xianrong Zheng

“…Pre-trained self-supervised speech models can extract general acoustic features, providing feature inputs for various speech downstream tasks. Spoofing speech detection, which is a pressing issue in the age of generative AI, requires both global information and local features of speech. …”
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IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced by Zhaoyang Han, Linlin Zhang, Qingyan Meng, Chongchang Wang, Wenxu Shi, Maofan Zhao

“…Currently, the development of deep learning has had a positive impact on remote sensing image change detection tasks, but many current methods still face challenges in effectively processing global and local features, especially in the task of building change detection in high-resolution images containing complex scenes. …”
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Network intrusion detection based on relative mutual K-nearest neighbor density peak clustering by Chunhua Ren, Changyuan Wang, Yang Yu, Wanan Yang, Ruiqi Guo

“…However, network intrusion data is highly random and dynamic, and the number and distribution structure of clusters are often difficult to determine in advance, resulting in limited detection accuracy and adaptability. To tackle this issue, this paper introduces a density peak clustering algorithm, RMKNN-FDPC, which integrates relative mutual K-nearest neighbor local density with a fuzzy allocation strategy for network intrusion detection, aiming to enhance the capability of identifying unknown attack patterns. …”
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Joint Detection and Removal of Specular Highlights using Vision Transformer with Multi-scale Patch Attention by Levent Karacan

“…Current methodologies typically use Convolutional Neural Network (CNN)-based Unet architectures for specular highlight detection. However, CNNs exhibit limitations in capturing global contextual information, despite excelling in local context analysis. …”
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An anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks by Qi Yang, Xuan Zhang, Jingfeng Qian, Qiang Ye

“…The proposed method adopts the statistical linear correlation analysis approach to detect anomaly nodes through the historical time synchronization information stored in local nodes. …”
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Syn‐Aug: An Effective and General Synchronous Data Augmentation Framework for 3D Object Detection by Huaijin Liu, Jixiang Du, Yong Zhang, Hongbo Zhang, Jiandian Zeng

“…Second, we propose a local augmentation technique (Local‐Aug) to generate local noise by rotating and scaling objects in the scene while avoiding collisions, which can improve generalisation performance. …”
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FD-IDS: Federated Learning with Knowledge Distillation for Intrusion Detection in Non-IID IoT Environments by Haonan Peng, Chunming Wu, Yanfeng Xiao

“…The proximal term restricts the deviation between local and global models, while KD utilizes the global model to steer the training process of local models. …”
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Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding by Bo She, Fuqing Tian, Weige Liang, Gang Zhang

“…A linear dimension reduction method called nonlocal orthogonal preserving embedding (NLOPE) and its nonlinear form named nonlocal kernel orthogonal preserving embedding (NLKOPE) are proposed and applied for condition monitoring and fault detection. Different from kernel orthogonal neighborhood preserving embedding (KONPE) and kernel principal component analysis (KPCA), the NLOPE and NLKOPE models aim at preserving global and local data structures simultaneously by constructing a dual-objective optimization function. …”
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FastADnet: Fast Anomaly Detection via Core-Feature Centered Cluster Reconstruction Network by Daeyeob Na, Youngjoon Yoo

“…By leveraging these features that represent local data distributions and their adjacent patch-features, our network effectively enhances anomaly detection along with reduced inference time. …”
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A Distributed Detection Method for Quality-related Faults in Complex Non-stationary Industrial Processes by Jie DONG, Daye LI, Yanmei WEI, Kaixiang PENG, Hui YANG

“…Then, the local detection results of each sub-block are fused through Bayesian inference. …”
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Fluctuating ecological networks: A synthesis of maximum‐entropy approaches for pattern detection and process inference by Tancredi Caruso, Giulio Virginio Clemente, Matthias C. Rillig, Diego Garlaschelli

“…This variability includes fluctuations in global properties such as the total number and intensity of interactions in the network but also in the number and intensity of local (i.e. node level) species interactions. Fluctuations of species' properties can significantly affect higher‐order network features, for example, robustness and nestedness, and should therefore be taken into account in null models for pattern detection and hypothesis testing. …”
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YOLO-GL for efficient multi-target detection in large FOV via hierarchical feature fusion by Xinyu Chen, Chengjun Dong, Tong Cui, Guangming Lv, Yan Ren

“…To address these limitations, this paper proposes YOLO-GL, an enhanced detection network featuring three key innovations: (1) a redesigned Parallelized Local-Global Multi-Level Fusion Module (C2f_gl) with a local-global attention mechanism for improved multi-scale feature representation; (2) a hierarchical feature fusion architecture; and (3) an adaptive feature fusion shuffling module (Multi-Scale Fusion Module and Adaptive Channel Shuffling Module, MSF&ACS) for dynamic optimization of cross-scale semantic relationships. …”
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Infrared Small-Target Detection via Improved Density Peak Clustering and Gray-Level Contribution by Lang Wu, Fan Fan, Jun Huang, Guoqiang Xiao

“…Then, we construct an unsupervised clustering model based on IDPC, which is tailored for detecting clustered targets and can accurately represent the local features of these targets. …”
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Remote Sensing-Based Detection and Analysis of Slow-Moving Landslides in Aba Prefecture, Southwest China by Juan Ren, Wunian Yang, Zhigang Ma, Weile Li, Shuai Zeng, Hao Fu, Yan Wen, Jiayang He

“…Additionally, 236 previously unknown landslides were detected and incorporated into the local geohazard database. …”
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Dual Branch Encoding Feature Aggregation for Cloud and Cloud Shadow Detection of Remote Sensing Image by Naikang Shi, Haifeng Lin, Huiwen Ji, Min Xia

“…Cloud detection is a critical preprocessing step for optical remote sensing imagery. …”
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YOLO-HPSD: A high-precision ship target detection model based on YOLOv10. by Manlin Zhu, Dezhi Han, Bing Han, Xiaohu Huang

“…This study proposes a high-precision ship target detection algorithm based on YOLOv10, named YOLO-HPSD (High-precision Ship Target Detection). …”
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CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images by Jianming Hu, Yangyu Wei, Wenbin Chen, Xiyang Zhi, Wei Zhang

“…Remote sensing target detection technology in cloud and mist scenes is of great significance for applications such as marine safety monitoring and airport traffic management. …”
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WRRT-DETR: Weather-Robust RT-DETR for Drone-View Object Detection in Adverse Weather by Bei Liu, Jiangliang Jin, Yihong Zhang, Chen Sun

“…To address the challenges of scale variation and visual degradation introduced by harsh weather, we propose WRRT-DETR, a weather-robust object detection framework optimized for small objects. Within this framework, we design a gated single-head global–local attention backbone block (GLCE) to fuse local convolutional features with global attention, enhancing small object distinguishability. …”
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PA2E: Real-Time Anomaly Detection With Hyperspectral Imaging for Food Safety Inspection by Jungi Lee, Myounghwan Kim, Jiseong Yoon, Kwangsun Yoo, Seok-Joo Byun

“…Deep learning techniques for anomaly detection have been extended to hyperspectral imaging. …”
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