An Improved Small Target Detection Algorithm Based on YOLOv8s by G. Ma, C. Xu, Z. Xu, X. Song

“…To address these challenges, this pa¬per proposes an enhanced small object detection algorithm, SOD-YOLO, based on YOLOv8s. First, the S_C2f_CAFM module is integrated into the feature extraction network, enabling the effective capture of fine-grained local features and broad contextual information, while simultaneously reducing model parameters and computational complexity. …”
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Detection method of potato leaf disease based on YOLOv5s by Jingtao Li, Hao Chen, Guisong Li, Yueqi Liu, Yanli Yang, Xia Liu, Chang Yi Wang

“… An improved leaf target detection method based on the YOLOv5s network is proposed to address the issues of low model detection accuracy and slow detection speed in potato leaf image target detection. …”
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Camera-Adaptive Foreign Object Detection for Coal Conveyor Belts by Furong Peng, Kangjiang Hao, Xuan Lu

“…CAFOD incorporates three main strategies: (1) Multi-View Data Augmentation (MVDA) simulates viewpoint variations during training, enabling the model to learn robust, viewpoint-invariant features; (2) Context Feature Perception (CFP) integrates local coal background information to reduce false detections outside the conveyor belt; and (3) Conveyor Belt Area Loss (CBAL) enforces explicit attention to the conveyor belt region, minimizing background interference. …”
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Circle Detection with Adaptive Parameterization: A Bottom-Up Approach by Lin Han, Yan Zhuang, Ke Chen, Yuhua Xie, Guoliang Liao, Guangfu Yin, Jiangli Lin

“…Circle detection remains a critical yet challenging task in computer vision, particularly under complex imaging conditions where existing measurement methods face persistent challenges in parameter configuration and noise resilience. …”
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BGLE-YOLO: A Lightweight Model for Underwater Bio-Detection by Hua Zhao, Chao Xu, Jiaxing Chen, Zhexian Zhang, Xiang Wang

“…Secondly, a global and local feature fusion module for small targets (BIG) is integrated into the neck network to preserve more feature information, reduce error information in higher-level features, and increase the model’s effectiveness in detecting small targets. …”
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A Novel Vision Sensing System for Tomato Quality Detection by Satyam Srivastava, Sachin Boyat, Shashikant Sadistap

“…Algorithm development consists of three major steps, preprocessing steps like noise rejection, segmentation and scaling, classification and recognition, and automatic disease detection and classification. Tomato samples have been collected from local market and data acquisition has been performed for data base preparation and various processing steps. …”
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Detection of cervical cell based on multi-scale spatial information by Gang Li, Xinyu Fan, Chuanyun Xu, Pengfei Lv, Ru Wang, Zihan Ruan, Zheng Zhou, Yang Zhang

“…To tackle this problem effectively, we propose a cervical cell detection method that utilizes multi-scale spatial information. …”
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Hybrid quantum enhanced federated learning for cyber attack detection by G. Subramanian, M. Chinnadurai

“…The conventional procedures developed to detects are centralized and often struggles with concerns like data privacy and communication overheads. …”
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Effectively Detecting Communities by Adjusting Initial Structure via Cores by Mei Chen, Zhichong Yang, Xiaofang Wen, Mingwei Leng, Mei Zhang, Ming Li

“…Community detection is helpful to understand useful information in real-world networks by uncovering their natural structures. …”
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A Comprehensive Joint Learning System to Detect Skin Cancer by Lubna Riaz, Hafiz Muhammad Qadir, Ghulam Ali, Mubashir Ali, Muhammad Ahsan Raza, Anca D. Jurcut, Jehad Ali

“…This research offers a joint learning system using Convolutional Neural Networks (CNN) and Local Binary Pattern (LBP) followed by its concatenation of all the extracted features through CNN and LBP architecture. …”
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Hybrid Islanding Detection of an Inverter-Based High-Noise Microgrid by Hossein Amini, Ali Mehrizi-Sani, Reza Noroozian

“…Unintentional islanding occurs when a microgrid continues operating independently after disconnection from the main grid, which can lead to voltage and frequency instability, power quality degradation, and safety risks. Few local and remote methods consider islanding detection in noisy environments. …”
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MST-YOLO: Small Object Detection Model for Autonomous Driving by Mingjing Li, Xinyang Liu, Shuang Chen, Le Yang, Qingyu Du, Ziqing Han, Junshuai Wang

“…A P2 detection layer is added to the neck part of the YOLOv8 model, and scale sequence feature fusion (SSFF) and triple feature encoding (TFE) modules are introduced to assist the model in better localizing small objects. …”
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An optimization-inspired intrusion detection model for software-defined networking by Hui Xu, Longtan Bai, Wei Huang

“…Currently, more and more intrusion detection systems based on machine learning and deep learning are being applied to SDN, but most have drawbacks such as complex models and low detection accuracy. …”
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A Method for Detecting Tomato Maturity Based on Deep Learning by Song Wang, Jianxia Xiang, Daqing Chen, Cong Zhang

“…The modeling of global and local information is realized through the self-attention mechanism, which improves the generalization ability and feature extraction ability of the model, thereby bringing higher detection accuracy. …”
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PV-YOLO: Lightweight YOLO for Photovoltaic Panel Fault Detection by Wang Yin, Shen Lingxin, Li Maohuan, Sun Qianlai, Li Xiaosong

“…In this paper, PV-YOLO is proposed to replace YOLOX’s backbone network, CSPDarknet53, with a transformer-based PVTv2 network to obtain local connections between images and feature maps to extract more edge-detail features of similar faults. …”
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Direct entanglement detection of quantum systems using machine learning by Yulei Huang, Liangyu Che, Chao Wei, Feng Xu, Xinfang Nie, Jun Li, Dawei Lu, Tao Xin

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Detection of axonal synapses in 3D two-photon images. by Cher Bass, Pyry Helkkula, Vincenzo De Paola, Claudia Clopath, Anil Anthony Bharath

“…To find the most appropriate techniques for this task, we compared several well-known algorithms for interest point detection and feature descriptor generation. The final algorithm proposed has the following main steps: (1) a Laplacian of Gaussian (LoG) based feature enhancement module to accentuate the appearance of boutons; (2) a Speeded Up Robust Features (SURF) interest point detector to find candidate locations for feature extraction; (3) non-maximum suppression to eliminate candidates that were detected more than once in the same local region; (4) generation of feature descriptors based on Gabor filters; (5) a Support Vector Machine (SVM) classifier, trained on features from labelled data, and was used to distinguish between bouton and non-bouton candidates. …”
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Ship Plate Detection Algorithm Based on Improved RT-DETR by Lei Zhang, Liuyi Huang

“…Comparative experiments conducted on a self-constructed dataset of 20,000 ship plate images show that, compared to the original RT-DETR, RT-DETR-HPA achieves a 3.36% improvement in mAP@50 (up to 97.12%), a 3.23% increase in recall (reaching 94.88%), and maintains real-time detection speed at 40.1 FPS. Compared with mainstream object detection models such as the YOLO series and Faster R-CNN, RT-DETR-HPA demonstrates significant advantages in high-precision localization, adaptability to complex scenarios, and real-time performance. …”
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Benchmarking Anomaly Detection Methods for Extracardiac Findings in Cardiac MRI by Edgar Pinto, Patrícia M. Costa, Catarina Silva, Vitor H. Pereira, Jaime C. Fonseca, Sandro Queirós

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Sliding Window Based Machine Learning System for the Left Ventricle Localization in MR Cardiac Images by Abdulkader Helwan, Dilber Uzun Ozsahin

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