Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments by Qifeng Yu, Yudong Han, Xinjia Gao, Wuguang Lin, Yi Han

“…To improve the detection performance of YOLO v5 for corrosion image features, this study investigates two enhanced models: EfficientViT-NWD-YOLO v5 and Gold-NWD-YOLO v5. …”
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PERE: Prior-Enhanced and Resolution-Extended Object Detection for Industrial Laminated Panel Scenes by Haoyu Wang, Yiqiang Wu, Jinshuo Liang, Xie Xie

“…Laminated panels are widely used in industry, and their quality inspection has traditionally relied on manual labor, which is time-consuming and prone to errors. Automated detection can significantly improve efficiency and reduce human error. …”
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MHS-VIT: Mamba hybrid self-attention vision transformers for traffic image detection. by Xude Zhang, Weihua Ou, Xiaoping Wu, Changzhen Zhang

“…The self-attention mechanism of the transformer is good at capturing long-distance spatial dependencies in images, which is crucial for understanding complex traffic scenes. Experimental results showed that MHS-VIT exhibited excellent performances in traffic image detection tasks. …”
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Research on Intrusion Detection Method Based on Transformer and CNN-BiLSTM in Internet of Things by Chunhui Zhang, Jian Li, Naile Wang, Dejun Zhang

“…Traditional Intrusion Detection Systems (IDS) face challenges in handling complex attack types, data imbalance, and feature extraction difficulties in IoT environments. …”
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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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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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A lightweight UAV target detection algorithm based on improved YOLOv8s model by Fubao Ma, Ran Zhang, Bowen Zhu, Xirui Yang

“…Second, Parameter Shared Convolution Head (PSC-Head) is designed to enhance detection efficiency and further minimize model size. …”
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Keypoint Detection and 3D Localization Method for Ridge-Cultivated Strawberry Harvesting Robots by Shuo Dai, Tao Bai, Yunjie Zhao

“…Additionally, incorporating the Gold-YOLO neck structure enhances multi-scale feature fusion, improving detection accuracy and enabling the method to adapt to complex environments. …”
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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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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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Urban object detection algorithm based on feature enhancement and progressive dynamic aggregation strategy by Luxuan Bian, Zijun Gao, Jue Wang, Bo Li

“…Traditional target detection models face challenges in recognizing urban high-altitude remote sensing targets due to complex background noise and significant variations in target scale. …”
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YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu, Ende Zhang

“…Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard computational resource constraints. …”
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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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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

“…This design ensures computational efficiency by using only dense layers for matrix-vector multiplications. …”
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MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion by Peicheng Shi, Wenchao Wu, Aixi Yang

“…3D object detection plays a pivotal role in achieving accurate environmental perception, particularly in complex traffic scenarios where single-modal detection methods often fail to meet precision requirements. …”
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Use of Business Intelligence as a Strategic Information Technology in Banking: Farud Discovery & Detection by علی محقر, کارو لوکس, ، فرید حسینی, آصف علی منشی

“…It also explains how BI can be effective technology in industries like banking to overcome critical issue like fraud discovery and detection. The methodology, based on BI, in the form of model named BI model for fraud discovery and detection has been devised and suggested at the end of paper along with it its details for overcoming the above mentioned issue in more effective and efficient manner.…”
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Cotton Leaf Disease Detection Using LLM-Synthetic Data and DEMM-YOLO Model by Lijun Gao, Tiantian Ran, Hua Zou, Huanhuan Wu

“…It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. …”
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Hybrid Optimization Method for Social Internet of Things Service Provision Based on Community Detection by Bahar Allakaram Tawfeeq, Amir Masoud Rahmani, Abbas Koochari, Nima Jafari Navimipour

“…The average response time of IGBSA‐CD presents that it is efficient in all three tasks is 0.04 s. It also has a consistently lower response time, even when the task is complex. …”
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Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks by Wasim Khan, Afsaruddin Mohd, Mohammad Suaib, Mohammad Ishrat, Anwar Ahamed Shaikh, Syed Mohd Faisal

“…This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism, thereby creating a more nuanced and efficient method for anomaly detection in complex networks. …”
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