Deciphering the complex molecular architecture of the genetically modified soybean FG72 through paired-end whole genome sequencing by Fan Wang, Shengtao Lu, Wenting Xu, Litao Yang

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DAU-YOLO: A Lightweight and Effective Method for Small Object Detection in UAV Images by Zeyu Wan, Yizhou Lan, Zhuodong Xu, Ke Shang, Feizhou Zhang

“…However, drone images typically exhibit challenges such as small object sizes, dense distributions, and high levels of overlap. Traditional object detection networks struggle to achieve the required accuracy and efficiency under these conditions. …”
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Improved Asphalt Pavement Crack Detection Model Based on Shuffle Attention and Feature Fusion by Tursun Mamat, Abdukeram Dolkun, Runchang He, Yonghui Zhang, Zulipapar Nigat, Hanchen Du

“…Pavement distress is one of the most serious and prevalent diseases in pavement road detection. However, traditional methods for crack detection often suffer from low efficiency and limited accuracy, necessitating improvements in the accuracy of existing crack detection algorithms. …”
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The Role of Sensor Technologies in Estrus Detection in Beef Cattle: A Review of Current Applications by Inga Merkelytė, Artūras Šiukščius, Rasa Nainienė

“…To enhance reproductive efficiency, advanced technologies are increasingly being integrated into cattle management. …”
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Dual‐fluorescence imaging and automated trophallaxis detection for studying multi‐nutrient regulation in superorganisms by Lior Baltiansky, Einav Sarafian‐Tamam, Efrat Greenwald, Ofer Feinerman

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Research on SeaTreasure Target Detection Technology Based on Improved YOLOv7-Tiny by Xiang Shi, Yunli Zhao, Jinrong Guo, Yan Liu, Yongqi Zhang

“…To address this challenge, this paper proposes a target detection algorithm for underwater sea treasures called UPA-YOLO, which aims to achieve accurate and efficient detection of underwater treasures and accelerates the inference through model transformation to enable the deployment of the detection model in edge devices. …”
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EGRN-YOLO: An Enhanced Multi-View Remote Sensing Detection Algorithm for Onshore Wind Turbines Based on YOLOv7 by Renzheng Xue, Haiqiang Xu, Qianlong Wu

“…Wind turbines, as the core components of wind power generation systems, play a crucial role in determining the overall generation efficiency and operational safety. However, the challenges posed by complex backgrounds, significant variations in the scale of wind turbine targets, and arbitrary orientations in unmanned aerial vehicle (UAV) remote sensing images have significantly increased the difficulty of real-time wind turbine detection. …”
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Detection of Surface Defects in Steel Based on Dual-Backbone Network: MBDNet-Attention-YOLO by Xinyu Wang, Shuhui Ma, Shiting Wu, Zhaoye Li, Jinrong Cao, Peiquan Xu

“…Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. …”
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DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks by Wasim Khan, Mohammad Haroon, Ahmad Neyaz Khan, Mohammad Kamrul Hasan, Asif Khan, Umi Asma Mokhtar, Shayla Islam

“…In this paper, we propose a new framework called DVAEGMM to detect anomalies on attributed networks. First, our framework utilizes a dual variational autoencoder for capturing the complex cross-modality relationships between node attributes and network structure, like vanilla autoencoders, but it also considers the potential data distribution and makes use of a generative adversarial network (GAN) for an adversarial regularization approach. …”
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A High-Accuracy Underwater Object Detection Algorithm for Synthetic Aperture Sonar Images by Jiahui Su, Deyin Xu, Lu Qiu, Zhiping Xu, Lixiong Lin, Jiachun Zheng

“…Compared with YOLOv8s, the proposed HAUOD algorithm can achieve <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.2</mn><mo>%</mo></mrow></semantics></math></inline-formula> higher accuracy with only <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>50.4</mn><mo>%</mo></mrow></semantics></math></inline-formula> model size, and reduce the computational complexity by half. Moreover, the HAUOD method exhibits significant advantages in balancing computational efficiency and accuracy compared to mainstream detection models.…”
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Classification of SERS spectra for agrochemical detection using a neural network with engineered features by Mateo Frausto-Avila, Monserrat Ochoa-Elias, Jose Pablo Manriquez-Amavizca, María del Carmen González-López, Gonzalo Ramírez-García, Mario Alan Quiroz-Juárez

“…Compared to other machine-learning algorithms, our approach offers reduced computational complexity while maintaining or exceeding the accuracy of more complex models. …”
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Fast Quality Detection of <i>Astragalus</i> Slices Using FA-SD-YOLO by Fan Zhao, Jiawei Zhang, Qiang Liu, Chen Liang, Song Zhang, Mingbao Li

“…Additionally, the integration of the SD module into the detection head optimizes parameter efficiency while improving detection performance. …”
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Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model by Yuan Liu, Yilong Liu, Xiaoyan Guo, Xi Ling, Qingyi Geng

“…On the AL10-DET dataset, SLF-YOLO achieves a mAP of 86.8%, striking an effective balance between detection accuracy and computational efficiency without increasing model complexity. …”
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Efficient Cow Body Condition Scoring Using BCS-YOLO: A Lightweight, Knowledge Distillation-Based Method by Zhiqiang Zheng, Zhuangzhuang Wang, Zhi Weng

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Real-time detection of Chinese cabbage seedlings in the field based on YOLO11-CGB by Hang Shi, Hang Shi, Changxi Liu, Changxi Liu, Miao Wu, Miao Wu, Hui Zhang, Hui Zhang, Hang Song, Hang Song, Hao Sun, Hao Sun, Yufei Li, Yufei Li, Jun Hu, Jun Hu

“…The model’s outputs are visualized using a heat map, and an Average Temperature Weight (ATW) metric is introduced to quantify the heat map’s effectiveness.Results and discussionComparative analysis reveals that YOLO11-CGB outperforms established object detection models like Faster R-CNN, YOLOv4, YOLOv5, YOLOv8 and the original YOLO11 in detecting Chinese cabbage seedlings across varied heights, angles, and complex settings. …”
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Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion by Chun Liu, Yuanliang Zhang, Jingfu Shen, Feiyue Liu

“…However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce the contrast between the target and the background. As a result, detecting infrared targets in complex marine environments remains challenging. …”
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RGE-YOLO enables lightweight road packaging bag detection for enhanced driving safety by Dangfeng Pang, Zhiwei Guan, Tao Luo, Yanhao Liang, Ruzhen Dou

“…However, research on detecting road packaging bags remains limited, and existing object detection models face challenges in small object detection, computational efficiency, and embedded deployment. …”
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MSAN-Net: An End-to-End Multi-Scale Attention Network for Universal Industrial Defect Detection by Zelu Wang, Ming Luo, Xinghe Xie, Yue Sun, Xinyu Tian, Zhengxuan Chen, Junwei Xie, Qinquan Gao, Tong Tong, Yue Liu, Tao Tan

“…Traditional manual visual inspection or single-task deep learning models were often struggled to balance detection efficiency and accuracy in complex industrial scenarios. …”
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An improved multi‐scale YOLOv8 for apple leaf dense lesion detection and recognition by Shixin Huo, Na Duan, Zhizheng Xu

“…Abstract Apple leaf lesions present a challenge for their detection and recognition because of their wide variety of species, morphologies, uneven sizes, and complex backgrounds. …”
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GLLR-HAD: Global-local low-rank integration for hyperspectral image anomaly detection by Yu Bai, Yanling Zhang, Lili Zhang, Tan Zhao

“…Abstract Hyperspectral imaging (HSI), with its rich spectral and spatial information, offers unique advantages for anomaly detection. However, existing methods often struggle to simultaneously model global structures and local discriminative features, which hampers their ability to detect structural anomalies and adapt to spectral variability in complex scenarios. …”
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