OptWake-YOLO: a lightweight and efficient ship wake detection model based on optical remote sensing images by Runxi Qiu, Nan Bi, Chaoyue Yin

“…However, challenges persist due to sea surface interference, meteorological conditions, and coastal structures, while practical applications demand lightweight models with fast detection speeds.MethodsWe propose OptWake-YOLO, a lightweight ship wake detection model with three key innovations: A RepConv-based RCEA module in the Backbone combining efficient layer aggregation with reparameterization to enhance feature extraction. …”
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Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network by Wuyi Chen, Huafeng Cai, Qiu Sun

“…In order to combat the difficulty of fault feature extraction and fault recognition in the field of bearing fault diagnosis, a bearing fault diagnosis method based on improved variational mode decomposition (VMD) and parallel hybrid neural network is proposed, which combines reweighted kurtosis (RK) with variable mode decomposition (VMD) and uses reweighted kurtosis as the evaluation index to select the decomposition times of variational mode decomposition, while removing part of the interference in the fault signal and retaining its impact characteristics. …”
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Bridge Damage Identification Using Time-Varying Filtering-Based Empirical Mode Decomposition and Pre-Trained Convolutional Neural Networks by Shenghuan Zeng, Jian Cui, Ding Luo, Naiwei Lu

“…The proposed method enhances the key frequency-domain features of signals and suppresses the interference of non-stationary noise on model training through adaptive denoising and time–frequency reconstruction. …”
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A Novel Few-Shot Learning Framework Based on Diffusion Models for High-Accuracy Sunflower Disease Detection and Classification by Huachen Zhou, Weixia Li, Pei Li, Yifei Xu, Lin Zhang, Xingyu Zhou, Zihan Zhao, Enqi Li, Chunli Lv

“…The rapid advancement in smart agriculture has introduced significant challenges, including data scarcity, complex and diverse disease features, and substantial background interference in agricultural scenarios. …”
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APPLICATION OF THE DUAL TREE COMPLEX WAVELET TRANSFORM AND MINIMUM ENTROPY DECONVOLUTION IN INCIPIENT FAULT DIAGNOSIS OF THE GEAR BOX by WANG ChaoGe, LIU TongTong, REN XuePing, ZHANG HaoDong, WANG JianGuo

“…However,due to the interference of noise,it was difficult to make a correct judgment from the spectrum of the components. …”
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STDF: Joint Spatiotemporal Differences Based on xLSTM Dendritic Fusion Network for Remote Sensing Change Detection by Yu Zhou, Shengning Zhou, Dapeng Cheng, Jinjiang Li, Zhen Hua

“…In addition, we introduce a hierarchical dendritic coordination module based on the dendritic neuron model, which leverages a dynamic weighting mechanism to flexibly integrate multiscale feature maps. This approach not only mitigates the noise interference caused by traditional rigid fusion methods but also strengthens the separation of features between change regions and the background, thereby improving detection accuracy and model stability. …”
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DermViT: Diagnosis-Guided Vision Transformer for Robust and Efficient Skin Lesion Classification by Xuejun Zhang, Yehui Liu, Ganxin Ouyang, Wenkang Chen, Aobo Xu, Takeshi Hara, Xiangrong Zhou, Dongbo Wu

“…Our visualization results further validate DermViT’s ability to locate lesions under interference conditions. By introducing a modular design that mimics a physician’s observation mode, DermViT achieves more logical feature extraction and decision-making processes for medical diagnosis, providing an efficient and reliable solution for dermoscopic image analysis.…”
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Knowledge-Driven Flood Intelligent Monitoring (KDFIM) Method: Analyzing the Kakhovka Dam Destruction Incident by Zhijun Jiao, Zhimei Zhang, Biyan Chen, Syed Amer Mahmood, Lixin Wu

“…First, KDFIM integrates both satellite imaging knowledge and ground object scattering knowledge to construct a spatiotemporal SAR feature fusion module, which is designed to mitigate the noise and interference present in the background and to extract multispectral SAR features for flood detection. …”
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SPANet: Spatial perceptual activation network for camouflaged object detection by Jianhao Zhang, Gang Yang, Xun Dai, Pengyu Yang

“…Then, a FIM that can effectively combine correlation information to suppress background interference and re‐encode to generate multi‐scale features is proposed. …”
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A Dual-Enhanced Hierarchical Alignment Framework for Multimodal Named Entity Recognition by Jian Wang, Yanan Zhou, Qi He, Wenbo Zhang

“…Although existing methods alleviate semantic deficiencies by optimizing image and text feature extraction and fusion, a fundamental challenge remains due to the lack of fine-grained alignment caused by cross-modal semantic deviations and image noise interference. …”
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Real time weed identification with enhanced mobilevit model for mobile devices by Xiaoyan Liu, Qingru Sui, Zhihui Chen

“…This step improves the clarity of images, particularly those with blurred edges or significant shadow interference. Following this, we introduce an optimized MobileViT model that incorporates the Efficient Channel Attention (ECA) module into the weed feature extraction network. …”
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A fine‐grained image classification method based on information interaction by Shuo Zhu, Xukang Zhang, Yu Wang, Zongyang Wang, Jiahao Sun

“…Abstract To enhance the accuracy of fine‐grained image classification and address challenges such as excessive interference factors within the dataset, inadequate extraction of local key features, and insufficient channel semantic association, a dual‐branch information interaction model that integrates convolutional neural networks (CNN) with Vision Transformers is proposed. …”
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YOLOv9-LSBN: An Improved YOLOv9 Model for Cotton Pest and Disease by Ruohong He, Fengkui Zhang, Jikui Zhu, Yulong Wang, Daorina Yang, Ting Zhang, Ping Li

“…A simplified weighted bidirectional feature pyramid network is integrated to strengthen bidirectional feature fusion and suppress background interference. …”
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Lightweight Dual-Backbone Detection Transformer for Infrared Insulator Detection by Boyang Zhang, Yanpeng Zhang, Liming Sun

“…This enhances the fusion of low and high-resolution features while optimizing feature representation in infrared images with complex backgrounds. …”
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A Combined Frame Difference and Convolution Method for Moving Vehicle Detection in Satellite Videos by Xin Luo, Jiatian Li, Xiaohui A, Yuxi Deng

“…Additionally, shallow large-scale feature maps, which use smaller receptive fields to focus on small targets and reduce background interference, are fed into the detection head. …”
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LWSARDet: A Lightweight SAR Small Ship Target Detection Network Based on a Position–Morphology Matching Mechanism by Yuliang Zhao, Yang Du, Qiutong Wang, Changhe Li, Yan Miao, Tengfei Wang, Xiangyu Song

“…However, ship detection under complex sea conditions still faces challenges, such as high-frequency noise interference and the limited computational power of edge computing platforms. …”
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A UAV Image Stitching Method for Complex Urban Environments by W. Niu, D. Qiu, R. Wu, R. Wu, Z. Wang, Y. Shi

“…To address the issues of uneven feature point distribution, environmental interference, and insufficient real-time performance in UAV image stitching in complex urban environments, this paper proposes an improved ORB algorithm based on Gaussian scale-space optimization and dynamic grid division, combined with a global geometric consistency optimization strategy. …”
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Design and Modeling of a Reconfigurable Multiple Input, Multiple Output Antenna for 24 GHz Radar Sensors by Mahmoud Shaban

“…The design features 2- and 4-port MIMO antenna each comprising a 1 × 8 microstrip patch array. …”
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Locating Building Change via Adaptive Frequency Enhancement by Lei Lu, Yuejie Li, Fei Yang, Haixiong Li, Guoqiang Wang, Kun Xie

“…It can adaptively enhance the information that favors the detection of architectural changes and suppresses irrelevant background noise interference. The entire network is designed with a classic U-shaped architecture, and two attention schemes are specifically designed, including spatial frequency attention module to regulate spatial-frequency weights in each level of feature maps, and the triple attention gate to comprehensively integrate spatial, channel, and frequency information. …”
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A Deep Learning-Based Method for Object Workpiece Recognition and Grasp Detection by Yunhan Li, Jingjing Lou, Zhiduan Cai, Chuan Ye, Ruichao Zhao, Yuhang Jiang

“…To address this, this paper proposes a target object detection network (YOLO-Net) that integrates feature fusion and attention mechanisms. First, a deep learning-based object detection model is developed to effectively mitigate the interference caused by uneven lighting, accurately extracting the features of the target objects. …”
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