Leveraging FastViT based knowledge distillation with EfficientNet-B0 for diabetic retinopathy severity classification by Jyotirmayee Rautaray, Ali B.M. Ali, Meenakshi Kandpal, Pranati Mishra, Rzgar Farooq Rashid, Farzona Alimova, Mohamed Kallel, Nadia Batool

“…This study presents FastEffNet, a novel framework that leverages transformer-based knowledge distillation (KD) to enhance DR severity classification while reducing computational complexity. The proposed approach employs FastViT-MA26 as the teacher model and EfficientNet-B0 as the student model, striking the ideal mix between accuracy and computational efficiency. …”
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Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models by Sollmann, Rahel

“…Here, I investigate whether closed spatial capture-recapture (SCR) and single season occupancy models are robust to ignoring temporal variation in detection probability. Ignoring temporal variation allows collapsing detection data across repeated sampling occasions, speeding up computations, which can be important when analyzing large datasets with complex models. …”
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Automated Surface Crack Identification of Reinforced Concrete Members Using an Improved YOLOv4-Tiny-Based Crack Detection Model by Sofía Rajesh, K. S. Jinesh Babu, M. Chengathir Selvi, M. Chellapandian

“…YOLOv4-tiny is faster and more efficient than its predecessors, offering real-time detection with reduced computational complexity. …”
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RL-Net: a rapid and lightweight network for detecting tiny vehicle targets in remote sensing images by Yaoyao Du, Li Chen, Xingxing Hao

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Automated graph-based detection of quantum control schemes: Application to molecular laser cooling by Anna Dawid, Niccoló Bigagli, Daniel W. Savin, Sebastian Will

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A lightweight and optimized deep learning model for detecting banana bunches and stalks in autonomous harvesting vehicles by Duc Tai Nguyen, Phuoc Bao Long Do, Doan Dang Khoa Nguyen, Wei-Chih Lin

“…Notably, the proposed model outperforms the previous detection models, offering high accuracy while optimizing computational efficiency. …”
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YOLO-Wheat: A More Accurate Real-Time Detection Algorithm for Wheat Pests by Yongkang Liu, Qinghao Wang, Qi Zheng, Yong Liu

“…Wheat pests exhibit considerable diversity and are often found in complex environmental contexts. Intraspecies variation among wheat pests can be substantial, while differences between species may be minimal, making accurate pest detection a difficult task. …”
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Research on Mine-Personnel Helmet Detection Based on Multi-Strategy-Improved YOLOv11 by Lei Zhang, Zhipeng Sun, Hongjing Tao, Meng Wang, Weixun Yi

“…In the complex environment of fully mechanized mining faces, the current object detection algorithms face significant challenges in achieving optimal accuracy and real-time detection of mine personnel and safety helmets. …”
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S₂Head: Small-Scale Human Head Detection Algorithm by Improved YOLOv8n Architecture by Yuteng Sui, Xinghua Shan, Linlin Dai, Hui Jing, Bo Li, Jianjun Ma

“…Existing head detection algorithms face challenges in accurately detecting small and densely packed head targets, resulting in lower detection accuracy in complex scenarios. …”
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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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Point-Level Fusion and Channel Attention for 3D Object Detection in Autonomous Driving by Juntao Shen, Zheng Fang, Jin Huang

“…PointPillars transforms point cloud data into a two-dimensional pseudo-image and employs a 2D CNN for efficient and precise detection. Nevertheless, this approach encounters two primary challenges: (1) the sparsity and disorganization of raw point clouds hinder the model’s capacity to capture local features, thus impacting detection accuracy; and (2) existing models struggle to detect small objects within complex environments, particularly regarding orientation estimation. …”
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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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Fine-grained detection and segmentation of civilian aircraft in satellite imagery using YOLOv8 by Ramesh Kumar Panneerselvam, Sarada Bandi, Sree Datta Siva Charan Doddapaneni

“…This paper presents that by developing an efficient YOLOv8-based model for aircraft detection, classification, and segmentation within the FAIR1M-2.0 dataset. …”
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Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments by Qifeng Yu, Yudong Han, Xinjia Gao, Wuguang Lin, Yi Han

“…YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion images often feature complex characteristics and high variability in detection targets, presenting significant challenges for YOLO v5 in recognizing and extracting corrosion features. …”
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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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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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LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving by Yunchuan Yang, Shubin Yang, Qiqing Chan

“…These components synergistically enhance small object perception and environmental context understanding without compromising network efficiency. Second, the neck features a hierarchical feature fusion module (HFFM) that establishes guided feature aggregation paths through hierarchical structuring, facilitating collaborative modeling between local structural information and global semantics for robust multi-scale object detection in complex traffic scenarios. …”
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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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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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Enhanced YOLOv5s Model for Improved Multi-Sized Object Detection in Road Scenes by Sangavi Sivanandham, Dharanibai Gunaseelan

“…Detecting objects in complex driving environments is crucial for autonomous vehicles to navigate safely. …”
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