Lightweight detection model for safe wear at worksites using GPD-YOLOv8 algorithm by Jian Xing, Chenglong Zhan, Jiaqiang Ma, Zibo Chao, Ying Liu

“…Furthermore, we adopt an Exponential Moving Average (EMA) SlideLoss function, which not only boosts accuracy but also ensures the stability of our safety wear detection model’s performance. …”
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Preclinical development of an immunoassay for the detection of TREM2: a new biomarker for Alzheimer’s disease by Jie Hu, Huimei Zeng, Jiaqi Lu, Tianpeng Li, Xue Liu, Yang Liu, Weihuan Wen, Weijun Shen, Hongying Chen, Zhicheng Chen

“…Impairment of TREM2 function aggravates the toxic effects of amyloid plaques, and its activation has been shown to reduce Aβ burden and memory deficits. …”
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A Multi-Feature Fusion Approach for Sea Fog Detection Under Complex Background by Shuyuan Yang, Yuzhu Tang, Zeming Zhou, Xiaofeng Zhao, Pinglv Yang, Yangfan Hu, Ran Bo

“…Sea fog is a natural phenomenon that significantly reduces visibility, posing navigational hazards for ships and impacting coastal activities. …”
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Improved RT-DETR Network for High-Quality Defect Detection on Digital Printing Fabric by Zebin Su, Yunlong Shao, Pengfei Li, Xingyi Zhang, Huanhuan Zhang

“…We also incorporated an inverted residual mobile block (iRMB) to integrate attention mechanisms into the network’s feature extraction process, and improved the bounding box loss function to enhance the model’s detection accuracy. …”
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Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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YOLOv8n-SMMP: A Lightweight YOLO Forest Fire Detection Model by Nianzu Zhou, Demin Gao, Zhengli Zhu

“…The experimental results reveal that, relative to the baseline model, the optimized lightweight model achieves a 3.3% enhancement in detection accuracy (mAP@0.5), slashes the parameter count by 31%, and reduces computational overhead by 33%. …”
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DAHD-YOLO: A New High Robustness and Real-Time Method for Smoking Detection by Jianfei Zhang, Chengwei Jiang

“…The wise–powerful intersection over union (Wise-PIoU) is adopted as the new bounding box regression loss function, resulting in quicker convergence speed and improved detection outcomes. …”
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Walnut Surface Defect Classification and Detection Model Based on Enhanced YOLO11n by Xinyi Ma, Zhongjia Hao, Shuangyin Liu, Jingbin Li

“…Finally, the EIoU loss function is adopted to enhance the model’s localization capability for irregularly shaped defects and reduce false detection rates by improving the scale sensitivity of bounding box regression. …”
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Defect Detection Algorithm for Electrical Substation Equipment Based on Improved YOLOv10n by Qingkai Meng, Tianren Fu, Kangning Li, Long Huang, Shaoqiang Chen

“…Finally, we implement Focal EIoU as the loss function to accelerate convergence and minimize losses. …”
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Surface Defect Detection Algorithm for Wind Turbine Blades Based on HSCA-YOLOv7 by Bing LI, Yunshan BAI, Kuan ZHAO, Congbin GUO, Yongjie ZHAI

“…The blade is one of the key components of the wind turbine, which is vulnerable to the impact of natural environmental factors, resulting in gel coat falling off, cracks, corrosion, and other damage and thus affecting the efficiency of wind power generation and the safety of wind turbine operation. A defect detection algorithm for wind turbine blades based on HSCA-YOLOv7 is proposed to address the issues of inconsistent defect scale, inaccurate positioning, and low detection accuracy in wind turbine blade images by aerial photography. …”
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Lightweight underwater object detection method based on multi-scale edge information selection by Shaobin Cai, Xin Zhou, Wanchen Cai, Liansuo Wei, Yuchang Mo

“…However, uneven lighting, color distortion, and noise interference in underwater environments severely impact image quality, significantly reducing detection robustness. With limited computational power and storage space, underwater equipment often cannot meet the demands for efficient processing. …”
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YOLO-SAG: An improved wildlife object detection algorithm based on YOLOv8n by Lingli Chen, Gang Li, Shunkai Zhang, Wenjie Mao, Mei Zhang

“…Training stability is enhanced by introducing the Softplus activation function, which increases detection accuracy; incorporating the AIFI enhances intra-scale feature interaction, reducing missed and false detections. …”
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MSOAR-YOLOv10: Multi-Scale Occluded Apple Detection for Enhanced Harvest Robotics by Heng Fu, Zhengwei Guo, Qingchun Feng, Feng Xie, Yijing Zuo, Tao Li

“…Furthermore, a Normalized Wasserstein Distance (NWD) loss function is proposed to effectively reduce missed detections of densely packed and overlapping targets. …”
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Improved YOLOv7-Tiny for the Detection of Common Rice Leaf Diseases in Smart Agriculture by Fuxu Guo, Jing Li, Xingcheng Liu, Sinuo Chen, Hongze Zhang, Yingli Cao, Songhong Wei

“…The MobileNetV3 lightweight network is introduced to replace the backbone network of YOLOv7-tiny, which reduces the size of the model parameters and improves the extraction capability of features of different sizes; the RCS-OSA is used to replace the original ELAN-1 module, which improves the extraction capability of interlayer features; the TSCODE detector head is designed to enhance the extraction capability of the model for small targets; and the MPDIoU loss function is used to improve the model’s convergence speed and effect. …”
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Abnormal sound detection method for coal mine belt conveyors based on convolutional autoencoder by SHEN Long, SHAN Haoran, PEI Wenliang, YANG Guixiang, WANG Yongli

“…Experimental results showed that, without abnormal sound samples involved in training, the proposed method achieved detection accuracies of 92.55%, 94.98%, and 93.60% for the idlers, reducer, and motor, respectively. …”
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Trust-driven approach to enhance early forest fire detection using machine learning by Tayyab Khan, Karan Singh, Bhoopesh Singh Bhati, Khaleel Ahmad, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…Our method seeks to reduce fire detection time and improve the reliability of the detection process. …”
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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

“…This mechanism aims to enhance the model’s feature extraction capability under challenging environmental conditions, thereby improving overall detection accuracy. To further improve localization performance and accelerate convergence, the loss function is also modified. …”
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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

“…Change detection is a key research area in remote sensing, focusing on identifying differences between images captured at different time points and generating change maps. …”
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Damage Detection in Beam Structures Based on Frequency-Domain Analysis Methods for Nonlinear Systems by Wenbo Zhang, Xiaoyue Guo, Liangliang Cheng, Bo Zhang

“…Conventional detection methods based on the frequency response function (FRF) in linear systems tend to fail when small early damage occurs in engineered structures. …”
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Innovative Approaches in Dental Care: Electrical Impedance Analysis (EIA) for Early Caries Detection by Liliana Sachelarie, Ioana Romanul, Daniela Domocos, Mihaela Moisa, Emilia-Albinita Cuc, Raluca Iurcov, Carmen Stadoleanu, Loredana Liliana Hurjui

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