MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection by Jun Liu, Haolin Li, Hao Liu, Jiuzhen Liang

“…An attention-guided network (AGNet) is a network that utilizes attention to guide information across different scales. Its feature extraction module employs a dual-branch information residual unit (DIRU) as a substitute for the conventional convolution block, which combines the feature extraction capabilities of global pooling and max pooling, reducing the number of parameters while also achieving a certain improvement in detection results. …”
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Neural Networks Detection of Low-Amplitude Components on ECG Using Modified Wavelet Transform by A. V. Mnevets, N. G. Ivanushkina

“… This study is devoted to identification of low amplitude components from ECG signals by different time-frequency analysis methods when main power spectrum falls on high-amplitude components. …”
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SECrackSeg: A High-Accuracy Crack Segmentation Network Based on Proposed UNet with SAM2 S-Adapter and Edge-Aware Attention by Xiyin Chen, Yonghua Shi, Junjie Pang

“…This function also applies Multi-Granularity Supervision by optimizing segmentation outputs at three different resolution levels, ensuring better feature consistency and improved model robustness across varying image scales. …”
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Driving by a Publicly Available RGB Image Dataset for Rice Planthopper Detection and Counting by Fusing Swin Transformer and YOLOv8-p2 Architectures in Field Landscapes by Xusheng Ji, Jiaxin Li, Xiaoxu Cai, Xinhai Ye, Mostafa Gouda, Yong He, Gongyin Ye, Xiaoli Li

“…Additionally, the Spatial and Channel Reconstruction Convolution (SCConv) was applied, replacing Convolution (Conv) in the C2f module of YOLOv8. …”
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Red Tide Detection Method Based on a Time Series Fusion Network Model: A Case Study of GOCI Data in the East China Sea by Tianhong Ding, Zhiqiang Xu, Yunjie Wang, Qinglian Hou, Xiangyong Liu, Fengshuang Ma

“…Additionally, an ECA channel attention mechanism is employed to fully exploit spectral features across different bands for deeper feature extraction. A novel feature extraction module, ASPC-DSC, combines atrous spatial pyramid convolution with depthwise separable convolution to effectively fuse multi-scale contextual features while improving computational efficiency. …”
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Transmission Line Anomaly Detection and Real-Time Monitoring System Combining Edge Computing and EfficientDet by Menghao Lin, Yang Ding, Tianle Wang, Yang Liu, Zewei Li

“…The experimental results show that the proposed method effectively improves recognition accuracy and recall rate, achieving over 88% on different types of transmission lines. The ablation experiment shows that adding K-means clustering algorithm and gradient equalization mechanism can significantly improve recognition accuracy.…”
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Visible-infrared person re-identification with region-based augmentation and cross modality attention by Yuwei Guo, Wenhao Zhang, Licheng Jiao, Shuang Wang, Shuo Wang, Fang Liu

“…Specifically, we propose a region-based data augmentation module PedMix to enhance pedestrian region coherence by mixing the corresponding regions from different modalities, thus generating more natural images. …”
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Identification of Low‐Value Defects in Infrared Images of Porcelain Insulators Based on STCE‐YOLO Algorithm by Shaotong Pei, Weiqi Wang, Chenlong Hu, Keyu Li, Haichao Sun, Mianxiao Wu, Bo Lan

“…And the multiple attention mechanism improved to the third generation of variability convolution is used to detect the head to improve the accuracy of the algorithm's target localization. …”
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AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view by Mostafa Jalilifar, Mahdi Sadeghi, Alireza Emami-Ardekani, Ahmad Bitarafan-Rajabi, Kouhyar Geravand, Parham Geramifar

“…Notably, MLP-based dose estimations closely matched ground truth data with < 15% differences in most tissues. The MLP-estimated dose values present a robust patient-specific dosimetry approach capable of swiftly predicting absorbed doses in different organs using WB planar images and a single oblique view. …”
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Online evaluation method for MMC submodule capacitor aging based on CapAgingNet by Xinlan Deng, Youhan Deng, Liang Qin, Weiwei Yao, Min He, Kaipei Liu

“…Subsequently, the CapAgingNet model is introduced, incorporating key technical modules to enhance performance: the Deep Stem module, which extracts larger receptive fields through multiple convolution layers and mitigates the impact of data sparsity in capacitor aging on feature extraction; the efficient channel attention (ECA) module, utilizing one-dimensional convolution for dynamic weighting to adjust the importance of each channel, thereby enhancing the ability of the model to process high-dimensional features in capacitor aging data; and the multiscale feature fusion (MSF) module, which integrates capacitor aging information across different scales by combining fine-grained and coarse-grained features, thus improving the capacity of the model to capture high-frequency variation characteristics. …”
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A High-Precision Defect Detection Approach Based on BiFDRep-YOLOv8n for Small Target Defects in Photovoltaic Modules by Yi Lu, Chunsong Du, Xu Li, Shaowei Liang, Qian Zhang, Zhenghui Zhao

“…Secondly, for the multi-scale characteristics of defects, the neck network is optimized by introducing a bidirectional weighted feature pyramid network (BiFPN), which adopts an adaptive weight allocation strategy to enhance feature fusion and improve the characterization of defects at different scales. Finally, the detection head part uses DyHead-DCNv3, which combines the triple attention mechanism of scale, space, and task awareness, and introduces deformable convolution (DCNv3) to improve the modeling capability and detection accuracy of irregular defects.…”
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PZS‐Net: Incorporating of Frame Sequence and Multi‐Scale Priors for Prostate Zonal Segmentation in Transrectal Ultrasound by Jianguo Ju, Qian Zhang, Pengfei Xu, Tiange Liu, Cheng Li, Ziyu Guan

“…Then, a multi‐scale fusion (MSF) module that utilizes three parallel branches with different atrous convolutions is designed. The MSF module is placed at the bottleneck layer to dynamically fuse multi‐scale context information from high‐level features. …”
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SDMSEAF-YOLOv8: a framework to significantly improve the detection performance of unmanned aerial vehicle images by Linxuan Li, Xiaoyu Liu, Xuan Chen, Fengjuan Yin, Bin Chen, Yufeng Wang, Fanbin Meng

“…Four detection heads are employed for tiny target detection, each responsible for different size ranges, so as to improve the accuracy and robustness of small target detection. …”
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PC3D-YOLO: An Enhanced Multi-Scale Network for Crack Detection in Precast Concrete Components by Zichun Kang, Kedi Gu, Andrew Yin Hu, Haonan Du, Qingyang Gu, Yang Jiang, Wenxia Gan

“…Furthermore, evaluations on the CRACKS_MANISHA and DECA datasets also confirm the proposed model’s strong generalization capability across different data domains.…”
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Fine-Grained Extraction of Coastal Aquaculture Ponds From Remote Sensing Images Using an Edge-Supervised Multi-task Neural Network by Jian Qi, Min Ji, Fengxiang Jin, Jianran Xu, Hanyu Ji, Juan Wang

“…Furthermore, transfer experiments with JL1 imagery from Jiangmen and Yantai demonstrate the strong generalization capability of the proposed method across different environments.…”
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A Sparse Feature-Based Mixed Signal Frequencies Detecting for Unmanned Aerial Vehicle Communications by Yang Wang, Yongxin Feng, Fan Zhou, Xi Chen, Jian Wang, Peiying Zhang

“…On this basis, complex dilated convolution and deconvolution are used successively to perform feature extraction on the separated signals, which enhances the receptive field and frequency resolution ability of the network for signals, reduces the interference between noise and different component signals, and realizes the accurate estimation of the number of components and carrier frequencies. …”
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A Multi-Scale Adaptive Fusion Network: End-to-End Interpretable Small-Sample Classifier for Motor Imagery EEG by Qiulei Han, Yan Sun, Ze Song, Hongbiao Ye, Tingwei Chen, Jian Zhao

“…The SCA module strengthens the model&#x2019;s ability to perceive features of different frequencies and spatio-temporal information through multi-scale feature interactions to extract richer primary feature representations. …”
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SMART DELAY PREDICTION: SUPERVISED MACHINE LEARNING SOLUTIONS FOR CONSTRUCTION PROJECTS by Pramodini Sahu, Dillip Kumar Bera, Pravat Kumar Parhi, Meenakshi Kandpal

“…In the study discussed here, different machine learning (ML) algorithms were investigated to foresee construction delays, and these include Gaussian Naïve Bayes, Adaboost, Logistic Regression, Gradient Boosting (GB), Random Forest (RF), Decision Tree (DT) and Extreme Gradient Boosting (XGBoost). …”
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SR-YOLO: Spatial-to-Depth Enhanced Multi-Scale Attention Network for Small Target Detection in UAV Aerial Imagery by Shasha Zhao, He Chen, Di Zhang, Yiyao Tao, Xiangnan Feng, Dengyin Zhang

“…First, the Space-to-Depth layer and Receptive Field Attention Convolution are combined, and the SR-Conv module is designed to replace the Conv module within the original backbone network. …”
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Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method by Aichen Wang, Yuanzhi Xu, Dong Hu, Liyuan Zhang, Ao Li, Qingzhen Zhu, Jizhan Liu

“…To address these issues, this study proposed an improved lightweight YOLO11n network and an optimized region tracking-counting method, which estimates the quantity of tomatoes at different maturity stages. An improved lightweight YOLO11n network was employed for tomato detection and semantic segmentation, which was combined with the C3k2-F, Generalized Intersection over Union (GIoU), and Depthwise Separable Convolution (DSConv) modules. …”
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