CSW-YOLO: A traffic sign small target detection algorithm based on YOLOv8. by Qian Shen, Yi Li, YuXiang Zhang, Lei Zhang, ShiHao Liu, Jinhua Wu

“…First, the bottleneck of the C2f module in the original yolov8 network is replaced with the residual Faster-Block module in FasterNet, and then the new channel mixer convolution GLU (CGLU) in TransNeXt is combined with it to construct the C2f-faster-CGLU module, reducing the number of model parameters and computational load; Secondly, the SPPF module is combined with the large separable kernel attention (LSKA) to construct the SPPF-LSKA module, which greatly enhances the feature extraction ability of the model; Then, by adding a small target detection layer, the accuracy of small target detection such as traffic signs is greatly improved; Finally, the Inner-IoU and MPDIoU loss functions are integrated to construct WISE-Inner-MPDIoU, which replaces the original CIoU loss function, thereby improving the calculation accuracy. …”
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I-YOLOv11n: A Lightweight and Efficient Small Target Detection Framework for UAV Aerial Images by Yukai Ma, Caiping Xi, Ting Ma, Han Sun, Huiyang Lu, Xiang Xu, Chen Xu

“…Therefore, this paper proposes a lightweight algorithm, I-YOLOv11n, based on YOLOv11n, which is systematically improved in terms of both feature enhancement and structure compression. The RFCBAMConv module that combines deformable convolution and channel–spatial attention is designed to adjust the receptive field and strengthen the edge features dynamically. …”
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YOLOv8n-SMMP: A Lightweight YOLO Forest Fire Detection Model by Nianzu Zhou, Demin Gao, Zhengli Zhu

“…Existing forest fire detection algorithms face limitations in capturing flame and smoke features in complex natural environments, coupled with high computational complexity and inadequate lightweight design for practical deployment. …”
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FPFS-YOLO: An Insulator Defect Detection Model Integrating FasterNet and an Attention Mechanism by Yujiao Chai, Xiaomin Yao, Manlong Chen, Sirui Shan

“…In this study, to mitigate parameter redundancy in the backbone of the YOLO11n model, the FasterNet lightweight network was introduced, and some convolution was embedded into the shallow network to enhance its feature extraction ability. …”
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BinaryViT: Binary Vision Transformer for Hyperspectral Image Classification by Xiang Hu, Taolin Liu, Zhe Guo, Yuxiang Tang, Yuanxi Peng, Tong Zhou

“…As a key technology for lightweight deep models, binary quantization achieves significant parameter compression and computational acceleration by binarizing activations and weights. …”
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EMB-YOLO: A Lightweight Object Detection Algorithm for Isolation Switch State Detection by Haojie Chen, Lumei Su, Riben Shu, Tianyou Li, Fan Yin

“…This module is designed with a lightweight structure, aimed at reducing the computational complexity and parameter count, thereby optimizing the model’s computational efficiency. …”
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Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures by Yudong Shi, Yinggan Zhang, Jiansen Wen, Zhou Cui, Jianhui Chen, Xiaochun Huang, Cuilian Wen, Baisheng Sa, Zhimei Sun

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Experimental Study on Compressive Capacity Behavior of Helical Anchors in Aeolian Sand and Optimization of Design Methods by Qingsheng Chen, Wei Liu, Linhe Li, Yijin Wu, Yi Zhang, Songzhao Qu, Yue Zhang, Fei Liu, Yonghua Guo

“…The XGBoost algorithm enabled the rigorous quantification of the governing geometric features of compressive capacity, culminating in a computational framework for the bearing capacity factor (<i>N</i>_q) and lateral earth pressure coefficient (<i>K</i>_u). …”
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Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO by Di Zhao, Yulin Cheng, Sizhe Mao

“…Finally, the Partial Convolution (PConv) lightweight module was also added to the feature fusion network, effectively reducing the model’s parameters and computational load, conserving computing resources, and improving detection speed and accuracy. …”
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Uncertainty analysis in the design of Type-IV composite pressure vessels for hydrogen storage by Yao Koutsawa, Lyazid Bouhala

“…The development and analysis of the tank model used conventional shell elements, starting from the liner’s inner dimensions and incorporating filament winding via the Abaqus Composite Layup feature. Critical design aspects, such as ply thickness, material properties and fiber orientation, were employed. …”
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Active accessibility: A review of operational measures of walking and cycling accessibility by David S. Vale, Miguel Saraiva, Mauro Pereira

“…While active travel has been shown to be associated with features of the built environment such as density and land-use mix, it is also associated with walking and cycling accessibility—which we designate as active accessibility. …”
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DEMNet: A Small Object Detection Method for Tea Leaf Blight in Slightly Blurry UAV Remote Sensing Images by Yating Gu, Yuxin Jing, Hao-Dong Li, Juntao Shi, Haifeng Lin

“…DEMNet introduces a dynamic convolution mechanism into the HGNetV2 backbone to form DynamicHGNetV2, enabling adaptive convolutional weight generation and improving feature extraction for blurry objects. An efficient EMAFPN neck structure further facilitates deep–shallow feature interaction while reducing the computational cost. …”
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A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis by Zhigang Zhang, Chunrong Xue, Xiaobo Li, Yinjun Wang, Liming Wang

“…Second, the multi-kernel clustering algorithm is used to compute the differences in input feature values, create pseudo-labels for the target domain samples, and update the CDAN network parameters through backpropagation, enabling the network to extract domain-invariant features. …”
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Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song, Yi Wu

“…Experimental results demonstrate that the improved model reduces the number of parameters by 38%, increases the frame processing speed (FPS) by 13.2%, and decreases the computational complexity (GFLOPs) by 42.8%, compared to the original model. …”
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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

“…Furthermore, these methods utilize diffusion networks to extract key features from dual-temporal remote images and generate change maps, yet they often overlook the model&#x0027;s parameter size and the time cost associated with iterative sampling. …”
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The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports. by Guomei Cui, Chuanjun Wang

“…Firstly, the data collected in this study come from high-precision sensors and computer simulation, involving key biomechanical parameters in sprint, such as step frequency, stride length and acceleration. …”
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Visual Detection on Aircraft Wing Icing Process Using a Lightweight Deep Learning Model by Yang Yan, Chao Tang, Jirong Huang, Zhixiong Cen, Zonghong Xie

“…WID-DeeplabV3+ adopts the lightweight MobileNetV3 as its backbone network to enhance the extraction of edge features in icing areas. Ghost Convolution and Atrous Spatial Pyramid Pooling modules are incorporated to reduce model parameters and computational complexity. …”
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TSDCA-BA: An Ultra-Lightweight Speech Enhancement Model for Real-Time Hearing Aids with Multi-Scale STFT Fusion by Zujie Fan, Zikun Guo, Yanxing Lai, Jaesoo Kim

“…Experimental results demonstrate that among lightweight models with fewer than 200K parameters, the proposed approach outperforms most existing methods in both denoising performance and computational efficiency, significantly reducing processing overhead. …”
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Investigation of Inlet Guide Vane Efficiency in Centrifugal Compressors for the Turbo Refrigeration Machines by Danilishin A.M., Kozhukhov Y. V., Fateeva E.S., Aksenov A.A.

“…The article investigates the influence of geometric parameters of inlet guide vanes (IGVs), including blade profiling and design features, on their operational efficiency. …”
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Advancing e-waste classification with customizable YOLO based deep learning models by P. Akhil Rajeev, Vivek Dharewa, D. Lakshmi, G. Vishnuvarthanan, Jayant Giri, T. Sathish, Mubarak Alrashoud

“…The ‘You Only Look Once’ (YOLO) methodology underpins our research, highlighting the distinctive architectural features of each model, including the CSPDarknet53 backbone, PANet, and advanced anchor-free detection. …”
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