Research on health monitoring of concrete structure based on G-S-G by Jiaqi Wang, Hongbi Kang, Kexin Li

“…On the basis of extracting the crack damage index, in order to verify the reliability of the sensitive feature index, starting from the two dimensions of damage texture feature and data expansion, through reverse research of the damage model, the damage index of accurately locating the crack damage was selected. …”
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Contrastive learning enhanced pseudo-labeling for unsupervised domain adaptation in person re-identification. by Xuemei Bai, Yuqing Zhang, Chenjie Zhang, Zhijun Wang

“…We first enhance the feature representation of the target domain samples based on the contrast learning technique to improve their discrimination in the feature space, thereby enhancing the cross-domain migration performance of the model. …”
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Improved Early-Stage Maize Row Detection Using Unmanned Aerial Vehicle Imagery by Lulu Xue, Minfeng Xing, Haitao Lyu

“…Next, an improved ROI-based feature point extraction method was used to eliminate residual noises and extract feature points. …”
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A Novel Integrated Fault Diagnosis Method Based on Digital Twins by Xiangrui Hu, Linglin Liu, Zhengyu Quan, Jinguo Huang, Jing Liu

“…The proposed model leverages a sliding window mechanism to capture both feature and temporal information, enhancing fault pattern recognition. …”
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Identification of lesion location and discrimination between benign and malignant findings in thyroid ultrasound imaging by Xu Yang, Hongliang Geng, Xue Wang, Lingxiao Li, Xiaofeng An, Zhibin Cong

“…Next, the CPCA mechanism is employed to reduce the interference of redundant information. Finally, a feature fusion network based on an aggregation-distribution mechanism is utilized to improve the learning capability of fine-grained features, enhancing the performance of early nodule detection. …”
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Research on underwater disease target detection method of inland waterway based on deep learning by Tao Yu, Yu Xie, Jinsong Luo, Wei Zhu, Jie Liu

“…Firstly, Bi-directional Feature Pyramid Network (BiFPN) is used to strengthen feature fusion and improve the accuracy of small target recognition. …”
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Research on intelligent segmentation method of coal body CT image fracture based on CBAM-UNet by Shuang Song, Yilun Xue, Suinan He, Xiang Ji, Xinshuang Cao, Guoying Liu, Juntao Chen, Hongjiao Chen

“…The convolutional block attention module is integrated into the model, enhancing fracture feature extraction across channel-spatial dimensions while suppressing coal matrix and mineral interference, effectively capturing cross-dimensional feature correlations to improve segmentation accuracy. …”
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CNN–Transformer Hybrid Architecture for Underwater Sonar Image Segmentation by Juan Lei, Huigang Wang, Zelin Lei, Jiayuan Li, Shaowei Rong

“…FLSSNet is built upon a CNN and Transformer backbone network, integrating four core submodules to address various technical challenges: (1) The asymmetric dual encoder–decoder (ADED) is capable of simultaneously extracting features from different modalities and systematically modeling both local contextual information and global spatial structure. (2) The Transformer feature converter (TFC) module optimizes the multimodal feature fusion process through feature transformation and channel compression. (3) The long-range correlation attention (LRCA) module enhances CNN’s ability to model long-range dependencies through the collaborative use of convolutional kernels, selective sequential scanning, and attention mechanisms, while effectively suppressing noise interference. (4) The recursive contour refinement (RCR) model refines edge contour information through a layer-by-layer recursive mechanism, achieving greater precision in boundary details. …”
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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. …”
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CMCD: A Consistency Model-Based Change Detection Method for Remote Sensing Images by Xiongjie Li, Weiying Xie, Jiaqing Zhang, Yunsong Li

“…In addition, we propose a new strategy for noise injection that concatenates with one remote sensing image rather than two, thereby reducing noise interference with feature information. We also develop a pruning strategy of skip connections and a top–down feature aggregation module to improve feature utilization efficiency. …”
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A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention by Yizong Wang, Zhengrong Xiao, Hong Wang, Fei Li, Jiya Tian

“…Secondly, the three-channel aggregated attention module Powerneck is proposed in the Neck section, which realizes efficient cross-scale feature interactions, effectively suppresses background noise interference, and improves robustness in complex field scenes through SimFusion_4in feature alignment, information fusion module (IFM) global context fusion, and Power channel dynamic weighting strategy. …”
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YOLO-UP: A High-Throughput Pest Detection Model for Dense Cotton Crops Utilizing UAV-Captured Visible Light Imagery by Chenglei Sun, Afizan Bin Azman, Zaiyun Wang, Xiaoxiao Gao, Kai Ding

“…Additionally, the model optimizes the neck design to re-duce semantic discrepancies between feature layers, improving small-target detection, and integrates large-kernel separable convolutions to bolster high-level feature processing. …”
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Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI by Haitian Gui, Han Jiao, Li Li, Xinhua Jiang, Tao Su, Zhiyong Pang

“…We adopted Faster RCNN as the architecture, introduced ROI aligning to minimize quantization errors and feature pyramid network (FPN) to extract different resolution features, added a bounding box quadratic regression feature map extraction network and three convolutional layers to reduce interference from tumor surrounding information, and extracted more accurate and deeper feature maps. …”
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Noise Robust Underwater Fishing Net Recognition Based on Range Gated Imaging by Zhensong Xu, Xinwei Wang, Liang Sun, Bo Song, Yue Zhang, Pingshun Lei, Jianan Chen, Jun He, Yan Zhou, Yuliang Liu

“…Comprehensive experiments on the test data show that SFNR-Net can effectively solve noise interference and achieve the best recognition accuracy of 96.28% compared with existing methods. …”
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SE-TransUNet-Based Semantic Segmentation for Water Leakage Detection in Tunnel Secondary Linings Amid Complex Visual Backgrounds by Renjie Song, Yimin Wu, Li Wan, Shuai Shao, Haiping Wu

“…Using a hybrid leakage dataset partitioned by k-fold cross-validation, the roles of SE-Block and ViT modules were examined through ablation experiments, and the model’s attention mechanism for leakage features was analyzed via Score-CAM heatmaps. Results indicate: (1) SE-TransUNet achieved mean values of 0.8318 (IoU), 0.8304 (Dice), 0.9394 (Recall), 0.8480 (Precision), 0.9733 (AUC), 0.8562 (MCC), 0.9218 (F1-score), and 6.53 (FPS) on the hybrid dataset, demonstrating robust generalization in scenarios with dent shadows, stain interference, and faint leakage traces. (2) Ablation experiments confirmed both modules’ necessity: The baseline model’s IoU exceeded the variant without the SE module by 4.50% and the variant without both the SE and ViT modules by 7.04%. (3) Score-CAM heatmaps showed the SE module broadened the model’s attention coverage of leakage areas, enhanced feature continuity, and improved anti-interference capability in complex environments. …”
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AG-Yolo: Attention-Guided Yolo for Efficient Remote Sensing Oriented Object Detection by Xiaofeng Wang, Chengshan Han, Liang Huang, Ting Nie, Xin Liu, Hao Liu, Mingxuan Li

“…An attention branch is further introduced to generate attention maps from shallow input features, guiding feature aggregation to focus on foreground objects and suppress complex background interference. …”
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The structure of the local detector of the reprint model of the object in the image by A. A. Kulikov

“…The existing identification systems and methods do not completely solve the problem of identification, namely, identification in difficult conditions: interference, lighting, various changes on the face, etc. …”
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Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model by Jie Ren, Delong Tian, Hexiang Zheng, Guoshuai Wang, Zekun Li

“…This model employs TTHHO to effectively navigate the search space and adaptively optimize network node positions, integrates CNN-LSSVM for feature extraction and regression analysis, and incorporates ABKDE for probability density function estimation and outlier detection, resulting in accurate interval probability prediction and enhanced model resilience to interference. …”
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Verification of a Probabilistic Model and Optimization in Long-Range Networks by José Luis Romero Vázquez, Abel García-Barrientos, José Alberto Del-Puerto-Flores, Francisco R. Castillo Soria, Roilhi F. Ibarra-Hernández, Ulises Pineda Rico, Ernesto Zambrano-Serrano

“…By incorporating these variables into a unified probabilistic framework, the model not only predicts packet loss and interference patterns but also provides insights into optimizing network parameters. …”
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Sorting misorganized books in libraries based on ant colony algorithm by Ling Chen

“…In the anti-noise interference test, the algorithm reduced the false detection rate of the Aerial dataset from 22.3 % of ACO to 9.8 % through the neighborhood gray-scale fusion mechanism of two-dimensional OTSU. …”
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