A Lightweight Approach to Comprehensive Fabric Anomaly Detection Modeling by Shuqin Cui, Weihong Liu, Min Li

“…In order to solve the problem of high computational resource consumption in fabric anomaly detection, we propose a lightweight network, GH-YOLOx, which integrates ghost convolutions and hierarchical GHNetV2 backbone together to capture both local and global anomaly features. …”
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Single-Scene SAR Image Data Augmentation Based on SBR and GAN for Target Recognition by Shangchen Feng, Xikai Fu, Yanlin Feng, Xiaolei Lv

“…On the other hand, ray tracing simulations offer high geometric accuracy and computational efficiency but struggle with low amplitude correctness, hindering accurate numerical feature extraction. …”
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OptWake-YOLO: a lightweight and efficient ship wake detection model based on optical remote sensing images by Runxi Qiu, Nan Bi, Chaoyue Yin

“…A Shared Lightweight Object Detection Head (SLODH) using parameter sharing and Group Normalization.ResultsExperiments on the SWIM dataset show OptWake-YOLO improves mAP50 by 1.5% (to 93.2%) and mAP50-95 by 2.9% (to 66.5%) compared to YOLOv11n, while reducing parameters by 40.7% (to 1.6M) and computation by 25.8% (to 4.9 GFLOPs), maintaining 303 FPS speed.DiscussionThe model demonstrates superior performance in complex maritime conditions through: RCEA's multi-branch feature extraction. …”
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GSF-YOLOv8: A Novel Approach for Fire Detection Using Gather-Distribute Mechanism and SimAM Attention by Caixiong Li, Dali Wu, Xing Zhang, Peng Wu

“…To address the current challenges in fire detection algorithms, including insufficient feature extraction, high computational complexity, limited deployment on resource-constrained devices, missed detections, false detections, and low accuracy, we developed a high-precision algorithm named GSF-YOLOv8. …”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…The ADown module dynamically adapts its downsampling strategy according to the feature characteristics, effectively reducing the number of parameters and computational complexity, while enhancing the model's ability to capture crack edges and fine textural details. …”
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Fluid Equation-Based and Data-Driven Simulation of Special Effects Animation by Yujuan Deng

“…For continuous image sequences, a linear dynamic model algorithm based on pyramidal optical flow is used to track the feature centers of the objects, and the spatial coordinates and motion parameters of the feature points are obtained by reconstructing the motion trajectories. …”
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SubsurfaceBreaks v. 1.0: a supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces by M. P. Michalak, C. Gerhards, P. Menzel

“…<p>The study presents a novel approach for fault detection on subsurface geological homoclinal interfaces (slopes) using a supervised learning algorithm and careful input variable (feature) selection. Synthetic faulted slopes are generated using Delaunay triangulation via the Computational Geometry Algorithms Library (CGAL), allowing for adjustments of parameters. …”
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Classification of Fritillaria thunbergii appearance quality based on machine vision and machine learning technology by DONG Chengye, LI Dongfang, FENG Huaiqu, LONG Sifang, XI Te, ZHOU Qin’an, WANG Jun

“…The tune-up in this study enhanced the detection performance of the model without increasing the number of parameters, computational complexity, or major changes to the original model. …”
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DC-YOLO: an improved field plant detection algorithm based on YOLOv7-tiny by Wenwen Li, Yun Zhang

“…Finally, we decoupled the detection head to minimize conflicts between features from different tasks. The results show that applying the proposed method to corn and weed datasets, the detection accuracy of the model reaches 95.7% mean Average Precision (mAP@0.5), the computational effort of the model is 13.083 Giga Floating-point Operations (GFLOPs), and the parameter size is 5.223 Millon (M), which is better than the rest of the mainstream light-weight target detection model.…”
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DeLA: An extremely faster network with decoupled local aggregation for large scale point cloud learning by Weikang Yang, Xinghao Lu, Binjie Chen, Chenlu Lin, Xueye Bao, Weiquan Liu, Yu Zang, Junyu Xu, Cheng Wang

“…Unlike simple pooling, neighborhood aggregation incorporates spatial relationships between points into the feature aggregation process, requiring repeated relationship learning and resulting in substantial computational redundancy. …”
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Intelligent deep learning architecture for precision vegetable disease detection advancing agricultural new quality productive forces by Jun Liu, Xuewei Wang, Qian Chen, Peng Yan, Dugang Guo

“…The Adaptive Detail Enhancement Convolution (ADEConv) module employs dynamic parameter adjustment to preserve fine-grained features while maintaining computational efficiency. …”
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Research on foreign object intrusion detection in railway tracks based on MSL-YOLO by Hongxia Niu, Dingchao Feng, Tao Hou

“…Specifically, a Multi-scale Shared Convolution Module (MSCM) is designed to replace SPPF, enhancing feature extraction while reducing parameters and computational cost. …”
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Prediction of porosity, hardness and surface roughness in additive manufactured AlSi10Mg samples. by Fatma Alamri, Imad Barsoum, Shrinivas Bojanampati, Maher Maalouf

“…Feature importance analysis on the compiled dataset using ANN revealed that laser power and scan speed are the most important features affecting relative density (e.g., porosity) and hardness, while scan speed and layer thickness significantly impact the surface roughness of the parts. …”
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LSEVGG: An attention mechanism and lightweight-improved VGG network for remote sensing landscape image classification by Yao Lu

“…Specifically, our approach incorporates depthwise separable convolutions and Squeeze-and-Excitation (SE) attention modules to create a model that is both computationally efficient and highly effective for landscape feature extraction. …”
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A Lightweight Multi-Scale Model for Speech Emotion Recognition by Haoming Li, Daqi Zhao, Jingwen Wang, Deqiang Wang

“…A_Inception combines the merits of Inception module and attention-based rectified linear units (AReLU) and thus can learn multi-scale features adaptively with low computational cost. Meanwhile, to extract most important emotional information, we propose a new multiscale cepstral attention and temporal-cepstral attention (MCA-TCA) module. …”
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An enhanced YOLOv8‐based bolt detection algorithm for transmission line by Guoxiang Hua, Huai Zhang, Chen Huang, Moji Pan, Jiyuan Yan, Haisen Zhao

“…The improved algorithm improves the accuracy of the bolt detection while reducing the computation complexity to achieve more lightweight model.…”
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Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization by Huaitao Shi, Yangyang Li, Peng Zhou, Shenghao Tong, Liang Guo, Baicheng Li

“…The potential well functions are mostly set fixed to reduce computational complexity, and the SR methods with fixed potential well parameters have better performances in stable working conditions. …”
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Vehicle detection in drone aerial views based on lightweight OSD-YOLOv10 by Yang Zhang, Xiaobing Chen, Su Sun, Hongfeng You, Yuanyuan Wang, Jianchu Lin, Jiacheng Wang

“…The proposed algorithm incorporates several key innovations: First, we employ online convolutional reparameterization to construct the OCRConv module and design a lightweight feature extraction structure, SPCC, to replace the conventional C2f module, thereby reducing computational load and parameter count. …”
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Conformal On-Body Antenna System Integrated with Deep Learning for Non-Invasive Breast Cancer Detection by Marwa H. Sharaf, Manuel Arrebola, Khalid F. A. Hussein, Asmaa E. Farahat, Álvaro F. Vaquero

“…A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. …”
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HPRT-DETR: A High-Precision Real-Time Object Detection Algorithm for Intelligent Driving Vehicles by Xiaona Song, Bin Fan, Haichao Liu, Lijun Wang, Jinxing Niu

“…This integration expands the model’s receptive field and enhances feature extraction without adding learnable parameters or complex computations, effectively minimizing missed detections of small targets. …”
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