Study on lightweight strategies for L-YOLO algorithm in road object detection by Ji Hong, Kuntao Ye, Shubin Qiu

“…Abstract With the increasing complexity of urban traffic, object detection has become critical in autonomous driving and intelligent traffic management. …”
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YOLO-SAD: Enhancing Small Aircraft Detection With Multi-Scale Context and Improved Gradient Flow by Xin Ning, Hongchang Ding, Xiaoqiang Guo

“…These results emphasize the robustness and efficiency of YOLO-SAD, establishing a new performance benchmark for small object detection in complex real-world scenarios.…”
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Anomaly detection in encrypted network traffic using self-supervised learning by Sadaf Sattar, Shumaila Khan, Muhammad Ismail Khan, Ainur Akhmediyarova, Orken Mamyrbayev, Dinara Kassymova, Dina Oralbekova, Janna Alimkulova

“…ET-SSL extends the use of SSL based traffic classification in order to improve detection performance while keeping computational complexity low through the maximization of the difference between normal and anomalous traffic. …”
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Fast and accurate obstacle detection based on stereo vision and deep learning by S. Badrloo, S. Badrloo

“…The ability to detect and avoid obstacles is essential for ensuring the safe and efficient navigation of mobile robots. …”
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A Method of Fatigue Driving State Detection Based on Deep Learning by XIONG Qunfang, LIN Jun, YUE Wei

“…Most of those algorithms have complex structure, low efficiency and weak adaptability in face of driver’s individual behavior habit. …”
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A Novel Approach to Speed Up Hampel Filter for Outlier Detection by Mario Roos-Hoefgeest Toribio, Alejandro Garnung Menéndez, Sara Roos-Hoefgeest Toribio, Ignacio Álvarez García

“…This paper proposes a novel Hampel filter variant that replaces the MAD with an original estimator (mMAD) that retains statistical robustness but is computationally more efficient. This reduces the filter’s computational complexity from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>N</mi><mo>·</mo><mi>w</mi><mo form="prefix">log</mo><mi>w</mi><mo>)</mo></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>N</mi><mo>·</mo><mi>w</mi><mo>)</mo></mrow></semantics></math></inline-formula>, where <i>N</i> is the data length and <i>w</i> the window size. …”
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A Tunnel Surface Diseases Detection Algorithm Based on YOLOv4 by LI Jia, QIU Xinhua, JI Yuwen

“…Aiming at the problem that tunnel environment is complex and variable and the contrast of surface image is low, which makes the traditional pattern recognition method cannot effectively detect diseases. …”
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Deep learning-assisted terahertz intelligent detection and identification of cancer tissue by Xingyu Wang, Yafei Xu, Rong Wang, Nuoman Tian, Zhengpeng Zhu, Shuting Fan, Liuyang Zhang, Ruqiang Yan, Xuefeng Chen

“…By combining the THz detection technique and artificial intelligence technique, here we propose a dense and efficient channel attention network (DECANet) framework-based THz diagnosis system for cancer prescreening. …”
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A Lightweight and Rapid Dragon Fruit Detection Method for Harvesting Robots by Fei Yuan, Jinpeng Wang, Wenqin Ding, Song Mei, Chenzhe Fang, Sunan Chen, Hongping Zhou

“…To enhance detection accuracy and satisfy the deployment constraints of edge devices, we propose YOLOv10n-CGD, a lightweight and efficient dragon fruit detection method designed for robotic harvesting applications. …”
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Improved YOLOv8-Based Algorithm for Citrus Leaf Disease Detection by Zhengbing Zheng, Yibang Zhang, Luchao Sun

“…Furthermore, it strikes an effective balance between enhancing detection accuracy, reducing model complexity, and maintaining a lightweight architecture, making them well-suited for efficient citrus leaf disease detection.…”
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Research on Intelligent Detection and Segmentation of Rock Joints Based on Deep Learning by Lei Peng, Haibo Wang, Chun Zhou, Feng Hu, Xiaoyang Tian, Zhu Hongtai

“…The current methods for detecting joints on tunnel face rely primarily on manual sketches, which are associated with issues of low detection efficiency and subjectivity. …”
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ANALYZING EEG SIGNALS FOR STRESS DETECTION USING RANDOM FOREST ALGORITHM by Fi Imanur Sifaunnufus Ms, Fitra Abdurrachman Bachtiar, Barlian Henryranu Prasetio

“…Detection of stress using EEG signals has gained much interest because of monitoring and early intervention. …”
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Insurance claims estimation and fraud detection with optimized deep learning techniques by P. Anand Kumar, S. Sountharrajan

“…Financial fraud has done significant damage to the global economy, thus threatening the stability and efficiency of capital markets. Deep learning techniques have proven highly effective in addressing these challenges to analyse complex patterns and relationships in extensive datasets. …”
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LNT-YOLO: A Lightweight Nighttime Traffic Light Detection Model by Syahrul Munir, Huei-Yung Lin

“…Moreover, most of the recent works only focused on daytime scenarios, often overlooking the significantly increased risk and complexity associated with nighttime driving. To address these critical issues, this paper introduces a novel approach for nighttime traffic light detection using the LNT-YOLO model, which is based on the YOLOv7-tiny framework. …”
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Spectral Clustering-Guided News Environments Perception for Fake News Detection by Kun Wang, Yuzhen Yang, Xiaoyang Wang

“…Then, we introduce a shared parameter multitask learning framework that treats different news environments as independent tasks and architects GRU bootstrapping modules with attention mechanisms to help aggregate features from different environments efficiently and interpretably. Finally, we provide textual perspective and stylistic perspective approaches during the detection process and soften the loss terms in multiple environments to alleviate the strict constraints, thus making it more compatible with the fake news detection task. …”
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Enhancing credit card fraud detection: highly imbalanced data case by Dalia Breskuvienė, Gintautas Dzemyda

“…It has been specifically designed to efficiently process and analyze vast and complex datasets commonly encountered in the financial sector, showcasing adaptability to the dynamic nature of big data environments. …”
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Improvement of RT-DETR model for ground glass pulmonary nodule detection. by Siyuan Tang, Qiangqiang Bao, Qingyu Ji, Tong Wang, Naiyu Wang, Min Yang, Yu Gu, Jinliang Zhao, Yuhan Qu, Siriguleng Wang

“…This article proposed an algorithm based on RT-DETR model with the following enhancement: 1) optimize the backbone network with FCGE blocks to increase the detection accuracy of small-sized and blurred edge nodules; 2) replace the AIFI module with HiLo-AIFI module to reduce redundant computation and improve the detection accuracy of pure ground glass pulmonary nodules and mixed ground glass pulmonary nodules; 3) replace the DGAK module with CCFF module to address the issue of capturing complex features and recognition of irregularly shaped ground glass nodules. …”
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Tower view object detection based on ECIOU structure embedded in YOLO by QIAN Jide, YAN Hao, LIANG Yan, ZENG Changchang, MOU Yihao

“…In view of the problems that the existing tower view target detection system is prone to large positioning deviation and low small target detection accuracy，this paper proposes an aircraft target detection method based on the ECIOU structure embedded in the YOLO v8 model from the tower view to improve the accuracy and efficiency of detection. …”
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Deep Ensemble Learning With Pruning for DDoS Attack Detection in IoT Networks by Makhduma F. Saiyedand, Irfan Al-Anbagi

“…DDoS attacks have evolved into complex multi-vector threats that high-volume and low-volume attack strategies, posing challenges for detection using traditional methods. …”
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CGTS: graph transformer-based anomaly detection in controller area networks by Xue Zhou, Guihe Qin, Yanhua Liang, Jiaru Song, Wanning Liu, Qingxin Liu

“…These results highlight CGTS can effectively detect multiple injection attacks and significantly improve the CAN bus intrusion detection performance.…”
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