Plasticulture detection at the country scale by combining multispectral and SAR satellite data by Alessandro Fabrizi, Peter Fiener, Thomas Jagdhuber, Kristof Van Oost, Florian Wilken

“…., greenhouse, tunnel) across Germany. The algorithm detected 103 103 ha of PMF and 37 103 ha of PCV in 2020, while a combination of agricultural statistics and surveys estimated a smaller plasticulture cover of around 100 103 ha in 2019. …”
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Automated Seizure Detection through EEG Analysis and Deep Learning Technique by Srinivas Nowduri, M. Madhusudhana Subramanyam

“…However, one of the challenges of automatic seizure detection using EEG analysis is extracting optimal features that can distinguish between different states of epilepsy. …”
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DCE-YOLOv8: Lightweight and Accurate Object Detection for Drone Vision by Jinsu An, Dong Hee Lee, Muhamad Dwisnanto Putro, Byeong Woo Kim

“…Object detection using drones is a sophisticated technology that employs a camera mounted on a drone in conjunction with a computer vision algorithm to pinpoint the precise location of an object and ascertain its type. …”
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A Universal Tire Detection Method Based on Improved YOLOv8 by Chi Guo, Mingxia Chen, Junjie Wu, Haipeng Hu, Luobing Huang, Junjie Li

“…To address the above problems, this paper proposes a lightweight YOLOv8n-SOI algorithm for tire defect detection. First, a similarity-based attention mechanism (SimAM) was introduced to the C2f block of the backbone network to improve the ability to extract the shape features of irregular tire defects in complicated backdrops. …”
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GNSS-Based Multi-Target RDM Simulation and Detection Performance Analysis by Jinxing Li, Qi Wang, Meng Wang, Youcheng Wang, Min Zhang

“…Furthermore, we introduce an Unscented Particle Filter (UPF) algorithm for dynamic target tracking and state estimation. …”
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Advancing Image Forensic: Detecting Facial Manipulations via Meso_KNN by Hazem Issa, Bayan Zarnahji

“…What distinguishes this model is its focus on important features in facial images that represent vital characteristics of facial manipulation. …”
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An Enhanced Deep Learning Model for Effective Crop Pest and Disease Detection by Yongqi Yuan, Jinhua Sun, Qian Zhang

“…Traditional machine learning methods struggle with plant pest and disease image recognition, particularly when dealing with small sample sizes, indistinct features, and numerous categories. This paper proposes an improved ResNet34 model (ESA-ResNet34) for crop pest and disease detection. …”
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Advanced intrusion detection in internet of things using graph attention networks by Aamir S. Ahanger, Sajad M. Khan, Faheem Masoodi, Ayodeji Olalekan Salau

“…In this paper, we present a novel perspective to IoT security by using a Graph-based (GB) algorithm to construct a graph that is evaluated with a graph-based learning Intrusion Detection System (IDS) incorporating a Graph Attention Network (GAT). …”
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Hybrid adaptive method for lane detection of degraded road surface condition by Khaled H. Almotairi

“…Subsequently, numerical features about the boundaries of the candidate lanes are extracted and used by a model of the k-nearest neighbor algorithm and the Gaussian process for final lane discovery. …”
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Bottom Detection Method of Side-Scan Sonar Image for AUV Missions by Huapeng Yu, Ziyuan Li, Dailin Li, Tongsheng Shen

“…In order to obtain the measurement parameters of the sea bottom geomorphology or underwater objects, the first step in side-scan sonar (SSS) image processing is bottom detection. Due to the complexity of the marine environment, the acoustic signals received by SSS are usually polluted by noises, which affect its image quality and make the extraction of image features difficult. …”
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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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System Architecture for Real-Time Face Detection on Analog Video Camera by Mooseop Kim, Deokgyu Lee, Ki-Young Kim

“…The proposed architecture is based on AdaBoost learning algorithm with Haar-like features and it aims to apply face detection to a low-cost FPGA that can be applied to a legacy analog video camera as a target platform. …”
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Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors by Michael Reich, Njira Lugogo, Laurie D Snyder, Megan L Neely, Guilherme Safioti, Randall Brown, Michael DePietro, Roy Pleasants, Thomas Li, Lena Granovsky

“…Purpose By using data obtained with digital inhalers, machine learning models have the potential to detect early signs of deterioration and predict impending exacerbations of chronic obstructive pulmonary disease (COPD) for individual patients. …”
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Automatic Paddy Planthopper Detection and Counting Using Faster R-CNN by Siti Khairunniza-Bejo, Mohd Firdaus Ibrahim, Marsyita Hanafi, Mahirah Jahari, Fathinul Syahir Ahmad Saad, Mohammad Aufa Mhd Bookeri

“…Therefore, this research presents a comprehensive assessment of an object detection algorithm specifically developed to precisely detect and quantify planthoppers. …”
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Multi-target personnel tracking algorithm for coal mine based on improved YOLOv7 and ByteTrack by Pengcheng QU, Jingzhao LI, Zechao LIU

“…In order to solve the problems of low accuracy and poor real-time performance of existing target tracking algorithms in the complex environment of coal mines, a YOLO-FasterNet+ByteTrack coal mine personnel tracking algorithm was proposed based on the Tracking by Detection (TBD) paradigm. …”
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A Survey of the Multi-Sensor Fusion Object Detection Task in Autonomous Driving by Hai Wang, Junhao Liu, Haoran Dong, Zheng Shao

“…In the field of autonomous driving, multi-sensor fusion object detection has become a hot research topic. To further explore the future development trends of multi-sensor fusion object detection, we introduce the mainstream framework Transformer model of the multi-sensor fusion object detection algorithm, and we also provide a comprehensive summary of the feature fusion algorithms used in multi-sensor fusion object detection, specifically focusing on the fusion of camera and LiDAR data. …”
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GES: A New Building Damage Data Augmentation and Detection Method Based on Extremely Imbalanced Data and Unique Spatial Distribution of Satellite Images by Xiaopeng Sha, Zhoupeng Guo, Xinqi Sang, Shuyu Wang, Yuliang Zhao

“…The experimental research on the proposed algorithm is conducted using the mainstream object detection models. …”
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Multi task detection method for operating status of belt conveyor based on DR-YOLOM by Yongan LI, Tengjie CHEN, Hongwei WANG, Zhihao ZHANG

“…The results show that compared to mainstream single detection algorithms, DR-YOLOM multi task detection algorithm has better comprehensive detection ability, and this algorithm can ensure high target recognition accuracy, segmentation accuracy, and appropriate inference speed with a small number of parameters. …”
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ACM-YOLOv10: Research on Classroom Learning Behavior Recognition Algorithm Based on Improved YOLOv10 by Beichen Qin, Haoyan Hu, Shaowen Du

“…To effectively integrate the research on learning engagement with teaching practices and accurately assess and analyze students’ learning behavior participation in the classroom to improve teaching quality, this paper proposes an improved YOLOv10 algorithm model, ACM-YOLOv10, targeting the issues of insufficient detection precision, missed detection, false detection, and slow speed of traditional recognition algorithms in classroom behavior detection under multi-scale scenarios and occluded targets. …”
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A Differential Biomarker Based on Recurrence Quantification Analysis of EEG Signal and Genetic Algorithm for Epilepsy Diagnosis by Vanithamani Palanisamy, A Ranichitra, Radhamani Ellapparaj

“…Experimental results showed that using the minimum norm and ε = 0.4 achieved a 100% discrimination rate for seizure detection. The transitivity recurrence feature proved highly effective in classifying normal and epileptic EEGs, making it an excellent biomarker for seizure detection with high diagnostic value.…”
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