MSUD-YOLO: A Novel Multiscale Small Object Detection Model for UAV Aerial Images by Xiaofeng Zhao, Hui Zhang, Wenwen Zhang, Junyi Ma, Chenxiao Li, Yao Ding, Zhili Zhang

“…In addition, the model uses lightweight convolution instead of standard convolution to reduce the model’s computation. …”
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YED-Net: Yoga Exercise Dynamics Monitoring with YOLOv11-ECA-Enhanced Detection and DeepSORT Tracking by Youyu Zhou, Shu Dong, Hao Sheng, Wei Ke

“…A dynamic adaptive anchor mechanism and an Efficient Channel Attention (ECA) module are introduced, while the depthwise separable convolution in the C3k2 module is optimized with a kernel size of 2. …”
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MGL-YOLO: A Lightweight Barcode Target Detection Algorithm by Yuanhao Qu, Fengshou Zhang

“…To address this issue, this paper proposes MGL-YOLO, a lightweight one-dimensional barcode detection network based on an improved YOLOv8, which aims to achieve a high detection accuracy at low computational cost. First, a new multi-scale group convolution (MSGConv) is designed and integrated into the C2f module to construct the MSG-C2f feature extraction module. …”
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Hierarchical Semi-Supervised Representation Learning for Cyber Physical Social Intelligence by Na Song, Jing Yang, Xuemei Fu, Xiangli Yang, Ying Xie, Shiping Wang

“…In the context of Cyber Physical Social Intelligence (CPSI), efficiently training and inferring from samples with limited labels poses critical challenges due to the scarcity and high cost of label acquisition for big data. The aim is to attain high accuracy at minimal cost, thereby enhancing adaptation to the CPSI scenario. …”
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Microgrid Load Forecasting Based on Improved Long Short-Term Memory Network by Qiyue Huang, Yuqing Zheng, Yuxuan Xu

“…Secondly, the LSTM gets improved from three aspects: multilayer convolution channel, lookahead optimizer, and AM weight. …”
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Adversarial sample generation algorithm for vertical federated learning by Xiaolin CHEN, Daoguang ZAN, Bingchao WU, Bei GUAN, Yongji WANG

“…To adapt to the scenario characteristics of vertical federated learning (VFL) applications regarding high communication cost, fast model iteration, and decentralized data storage, a generalized adversarial sample generation algorithm named VFL-GASG was proposed.Specifically, an adversarial sample generation framework was constructed for the VFL architecture.A white-box adversarial attack in the VFL was implemented by extending the centralized machine learning adversarial sample generation algorithm with different policies such as L-BFGS, FGSM, and C＆amp;W.By introducing deep convolutional generative adversarial network (DCGAN), an adversarial sample generation algorithm named VFL-GASG was designed to address the problem of universality in the generation of adversarial perturbations.Hidden layer vectors were utilized as local prior knowledge to train the adversarial perturbation generation model, and through a series of convolution-deconvolution network layers, finely crafted adversarial perturbations were produced.Experiments show that VFL-GASG can maintain a high attack success while achieving a higher generation efficiency, robustness, and generalization ability than the baseline algorithm, and further verify the impact of relevant settings for adversarial attacks.…”
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YOLOv8-RBean: Runner Bean Leaf Disease Detection Model Based on YOLOv8 by Hongbing Chen, Haoting Zhai, Jinghuan Hu, Hongrui Chen, Changji Wen, Yizhe Feng, Kun Wang, Zhipeng Li, Guangyao Wang

“…Moreover, the model reduces the number of parameters to 2.71 M and computational cost to 7.5 GFLOPs, representing reductions of 10% and 7.4% compared to the baseline model. …”
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A novel lightweight model for tea disease classification based on feature reuse and channel focus attention mechanism by Junjie Liang, Renjie Liang, Dongxia Wang

“…This leads to a significant increase in computational cost and affects the running speed on edge computing devices. …”
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Enhanced YOLOv8-based pavement crack detection: A high-precision approach. by ZuXuan Zhang, HongLi Zhang, TongJia Zhang

“…At present, the repair of cracks is still implemented manually, which has the problems of low identification efficiency and high labor cost. Crack detection is the key to realize the mechanical and intelligent crack repair. …”
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LANA-YOLO: Road defect detection algorithm optimized for embedded solutions by Paweł TOMIŁO

“…In addition, the article presents Basic Involution Block (BIB) that uses the involution layer to provide better performance at a lower cost than convolution layers. The model was compared with other architectures on a public dataset as well as on a dataset specially created for these purposes. …”
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Investigation and Implementation of Multi-Stereo Camera System Integration for Robust Localization in Urban Environments by A. Rai, E. Mounier, E. Mounier, P. R. M. de Araujo, A. Noureldin, A. Noureldin, K. Jain

“…Beyond these complexities, environmental conditions like signal-blocking skyscrapers, unpredictable obstacles, and the high costs of precision sensing add further convolution. …”
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ANN-SVM-IP: An Innovative Method for Rapidly and Efficiently Detecting and Classifying of External Defects of Apple Fruits by Nashaat M. Hussain Hassan, Mohamed M. Hassan Mahmoud, Mohamed A. Ismeil, M. Mourad Mabrook, A. A. Donkol, A. M. Mabrouk

“…The proposed strategy combines accuracy, rapidity, and affordable implementation cost. The first phase attempts to detect exterior defects in apples by applying two proposed convolution kernels that were capable of identifying damaged sections of apples. …”
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Single-Pixel Imaging Based on Enhanced Multi-Network Prior by Jia Feng, Qianxi Li, Jiawei Dong, Qing Zhao, Hao Wang

“…Owing to the high sensitivity, low cost, and wide spectrum, it acquires extensive applications across various domains. …”
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A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5 by Burhan Duman

“…The architecture integrates ghost convolution and ECA blocks to improve feature extraction with less computational overhead in the network’s backbone. …”
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A lightweight steel surface defect detection network based on YOLOv9 by Tianyi Zheng, Ling Yu, Yongbao Shi, Fanglin Niu

“…To address the issues of high computational cost and low detection accuracy in current steel defect detection models, we propose a YOLOv9-based steel defect detection algorithm, CCSS-YOLO. …”
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A lightweight UAV target detection algorithm based on improved YOLOv8s model by Fubao Ma, Ran Zhang, Bowen Zhu, Xirui Yang

“…First, Cross Stage Partial Convolutional Neural Network (CNN) Transformer Fusion Net (CSP-CTFN) is proposed. …”
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MA-YOLO: A Pest Target Detection Algorithm with Multi-Scale Fusion and Attention Mechanism by Yongzong Lu, Pengfei Liu, Chong Tan

“…The SDConv module reduces computational costs through depthwise separable convolution and dynamic group convolution while enhancing local feature extraction. …”
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LAVID: A Lightweight and Autonomous Smart Camera System for Urban Violence Detection and Geolocation by Mohammed Azzakhnini, Houda Saidi, Ahmed Azough, Hamid Tairi, Hassan Qjidaa

“…Centralized architectures do not present the ideal solution due to the high cost, processing time issues, and network bandwidth overhead. …”
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The TDGL Module: A Fast Multi-Scale Vision Sensor Based on a Transformation Dilated Grouped Layer by Leilei Xie, Fenghua Zhu, Zhixue Wang

“…The TDGL is built upon the Global Layer Normalization Convolution (GLConv) unit, which mitigates internal covariate shift by introducing scaling and offset parameters, modifying dilation strategies, and employing grouped convolution. …”
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Detection of SAR Image Multiscale Ship Targets in Complex Inshore Scenes Based on Improved YOLOv5 by Zhixu Wang, Guangyu Hou, Zhihui Xin, Guisheng Liao, Penghui Huang, Yonghang Tai

“…Finally, to reduce the number of parameters and computational cost during model training, the normal convolution in the neck part is replaced with Ghost convolution. …”
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