MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection by Hantao Ding, Shengfeng Lou, Hairong Ye, Yanbing Chen

“…The explicit spatiotemporal encoding scheme further improves temporal alignment accuracy by 2.4%, contributing to enhanced anomaly localization and overall detection accuracy. Additionally, the proposed framework achieves a 14.3% reduction in FLOPs and demonstrates 18.7% faster convergence during training, highlighting its practical value for real-world deployment. …”
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Intelligent islanding detection framework for smart grids using wavelet scalograms and HOG feature fusion by Kumaresh Pal, Kumari Namrata, Ashok Kumar Akella, Akshit Samadhiya, Ahmad Taher Azar, Mohamed Tounsi, Naglaa F. Soliman, Walid El-Shafai

“…Abstract Unintended islanding presents substantial operational and safety risks in modern electrical distribution networks, particularly as distributed generation (DG) sources increasingly match or nearly match local load requirements. Conventional islanding detection schemes (IDS) often fail under balanced load-generation conditions, resulting in significant undetected events, commonly referred to as the non-detection zone (NDZ). …”
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YOLO-UIR: A Lightweight and Accurate Infrared Object Detection Network Using UAV Platforms by Chao Wang, Rongdi Wang, Ziwei Wu, Zetao Bian, Tao Huang

“…However, existing methods face challenges such as insufficient accuracy or low computational efficiency, particularly in the detection of small objects. This paper proposes a lightweight and accurate UAV infrared object detection model, YOLO-UIR, for small object detection from a UAV perspective. …”
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VJDNet: A Simple Variational Joint Discrimination Network for Cross-Image Hyperspectral Anomaly Detection by Shiqi Wu, Xiangrong Zhang, Guanchun Wang, Puhua Chen, Jing Gu, Xina Cheng, Licheng Jiao

“…Cross-image HAD aims to perform anomaly detection on unknown hyperspectral images after a single training process on the network, thereby improving detection efficiency in practical applications. …”
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A Systematic Review of Aflatoxin B1 Contamination in Livestock Feed and Detection Methods in Ethiopia by Sisay Weldegebriel Zeweld, Enquebaher Kassaye Tarekegn, Meressa Abraha Welearegay

“…This study focuses on specific cereal grains, such as maize and groundnuts, which are commonly used in livestock feed but have been underexplored in terms of their role in AFB1 contamination, especially under local farming and storage conditions. A literature search was conducted from June 2024 to November 2024, utilizing databases like Google Scholar and African Journals Online. …”
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Patch-Wise-Based Self-Supervised Learning for Anomaly Detection on Multivariate Time Series Data by Seungmin Oh, Le Hoang Anh, Dang Thanh Vu, Gwang Hyun Yu, Minsoo Hahn, Jinsul Kim

“…By systematically learning both local and global dependencies, the proposed method strikes an effective balance between feature representation and anomaly detection accuracy, making it a valuable tool for real-world multivariate time series applications.…”
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Anomaly Detection Method for Hydropower Units Based on KSQDC-ADEAD Under Complex Operating Conditions by Tongqiang Yi, Xiaowu Zhao, Yongjie Shi, Xiangnan Jing, Wenyang Lei, Jiang Guo, Yang Meng, Zhengyu Zhang

“…Then, an ADEAD algorithm is designed, which incorporates local density information and improves anomaly detection accuracy and stability through multi-model ensemble and density-adaptive strategies. …”
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Flexible Polymer-Based Electrodes for Detecting Depression-Related Theta Oscillations in the Medial Prefrontal Cortex by Rui Sun, Shunuo Shang, Qunchen Yuan, Ping Wang, Liujing Zhuang

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A dual-domain perception gate-controlled adaptive fusion algorithm for road crack detection by Ziyang Zhang, Yong’an Feng

“…Abstract Road crack detection presents critical challenges, including diverse defect patterns and complex anomaly characteristics. …”
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LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach by Mingxin Liu, Mingxin Liu, Yujie Wu, Ruixin Li, Cong Lin, Cong Lin

“…Finally, we design a new detection head, CLLAHead, which reduces computational costs and strengthens the robustness of the model through cross-layer local attention. …”
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AFDR-Det: Adaptive Feature Dual-Refinement Oriented Detector for Remote Sensing Object Detection by Jie Yang, Li Zhou, Yongfeng Ju

“…Arbitrary-oriented object detection is a significant and challenging remote sensing image detection task. …”
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Time-Distributed Vision Transformer Stacked With Transformer for Heart Failure Detection Based on Echocardiography Video by Mgs M. Luthfi Ramadhan, Adyatma W. A. Nugraha Yudha, Muhammad Febrian Rachmadi, Kevin Moses Hanky Jr Tandayu, Lies Dina Liastuti, Wisnu Jatmiko

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Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures by Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

“…Automated pavement crack detection faces significant challenges due to the complex shapes of crack patterns, their similarity to non-crack textures, and varying environmental conditions such as lighting and noise. …”
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Deep Learning-Based Detection and Digital Twin Implementation of Beak Deformities in Caged Layer Chickens by Hengtai Li, Hongfei Chen, Jinlin Liu, Qiuhong Zhang, Tao Liu, Xinyu Zhang, Yuhua Li, Yan Qian, Xiuguo Zou

“…Beyond local detection, a digital twin remote monitoring system was developed, combining three-dimensional (3D) modeling, the Internet of Things (IoT), and cloud-edge collaboration to create a dynamic, real-time mapping of physical layer farms to their virtual counterparts. …”
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Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis by Junjie Ma, Fang Yang, Rong Yang, Yuan Li, Yongjing Chen

“…The fusion of DL models leverages robust feature extraction and global contextual understanding that is best suited for image data to improve the accuracy of cancer detection systems. The proposed approach then employs the Explainable Artificial Intelligence (XAI) technique, namely Local Interpretable Model-Agnostic Explanations (LIME), to present insights and transparency through visualizations into the model's decision-making process. …”
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PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets by Xue Xia, Ning Zhang, Zhibin Guan, Xin Chai, Shixin Ma, Xiujuan Chai, Tan Sun

“…The mean average precision (mAP) of 0.985 reflected the model's overall effectiveness in detecting all classes of aggressive behaviors. The model achieved a detection speed FPS of 69 f/s, with model complexity measured by 7.2 G floating-point operations (GFLOPs) and parameters (Params) of 2.63 million. …”
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Lightweight deep neural network for contour detection and extraction of wheat spikes in complex field environments by Xin Xu, Haiyang Zhang, Jiangchuan Lu, Ziyi Guo, Juanjuan Zhang, Jibo Yue, Hongbo Qiao, Xinming Ma

“…Method Building on two-year multi-angle wheat spike imagery, we propose an enhanced YOLOv9-LDS multi-scale object detection framework. The algorithm innovatively constructs a lightweight depthwise separable network (LDSNet) as backbone, balancing computational efficiency and accuracy through channel re-parameterization strategy; incorporates an Efficient Local Attention (ELA) module to build feature enhancement networks, and employs dual-path feature fusion mechanisms to strengthen edge texture responses, significantly improving discrimination of overlapping spikes and complex backgrounds. …”
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Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning by Muhammad Umair, Jawad Ahmad, Nada Alasbali, Oumaima Saidani, Muhammad Hanif, Aizaz Ahmad Khattak, Muhammad Shahbaz Khan

“…Proposed framework keeps data on local nodes and only exchanges intermediate representations. …”
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GHFormer-Net: Towards more accurate small green apple/begonia fruit detection in the nighttime by Meili Sun, Liancheng Xu, Rong Luo, Yuqi Lu, Weikuan Jia

“…Specifically, PVTv2-B1 based on Transformer is applied as the backbone network to extract feature information from the global receptive, which breaks the limitation that spatial convolution is utilized to extract information from the local area; Next, with the help of FPN, shallow features and high-level features with rich semantic information are incorporated by lateral connections and a top-down structure to generate multi-scale feature maps; Then, a detector of RetinaNet is applied to detect green fruits. …”
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Pothole detection and segmentation in the Bushveld Complex using physics-based data augmentation and deep learning by Glen T. Nwaila, Musa S.D. Manzi, Emmanuel John M. Carranza, Raymond J. Durrheim, Hartwig E. Frimmel

“…Potholes are local depression structures that disrupt stratigraphic continuity, such as in layered igneous intrusions. …”
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