Accuracy and cost comparison of 3D-printed guides in complex spinal deformity correction: direct vs indirect design by Wei Yang, Wei Yang, Wei Yang, Wei Guo, Wei Guo, Wen-Jun Wu, Rong Ma, Zemin Wang, Honglai Zhang, Wanzhong Yang, Zhaohui Ge, Zhaohui Ge

“…Among these techniques, patient-specific guides, which feature pre-defined and pre-validated trajectories, present an attractive solution for achieving precision in screw placement and osteotomies.MethodsCT scan data (DICOM format) from 10 patients with complex and severe spinal deformities were selected. …”
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Unsupervised Hybrid VAE-Based Anomaly Detection for Vehicle Onboard LiDAR Sensors by Nourhen Sboui, Hakim Ghazzai, Mohamed Hadded, Mourad Elhadef, Gianluca Setti

“…The model strikes a good balance between complexity and efficiency, achieving a 10% accuracy improvement compared to existing models, with an accuracy of 95.1% and an F1-score of 82.6%. …”
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ASSESSMENT OF COLLECTION NURSERY OF WINTER GARLIC ON ECONOMY VALUABLE SIGNS by L. I. Gerasimova, A. F. Agafonov, Т. М. Seredin

“…Results of long-term researches by laboratory selection of onions cultures (VNIISSOK) on studying, assessment and selection exemplars grades of garlic winter, collected from different regions of Russia and the CIS countries, on a complex of signs are presented in article (winter hardiness, efficiency, to quality of production, resistance to wreckers and diseases).…”
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Detecting Fraudulent Transactions for Different Patterns in Financial Networks Using Layer Weigthed GCN by Shaziya Islam, Gagan Raj Gupta, Apu Chakraborty, Santosh Singh, Anisha Soni, Chhavi Patle

“…Additionally, the flexible architecture of LayerWeighted-GCN enhances its ability to model complex financial relationships, improving fraud detection accuracy and robustness. …”
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Explainable one-class feature extraction by adaptive resonance for anomaly detection in quality assurance. by Hootan Kamran, Dionne Aleman, Chris McIntosh, Tom Purdie

“…We introduce a novel one-class classification framework, using an adaptive neural network architecture, that outperforms both traditional binary and standard one-class classification methods in this imbalanced and complex context, despite the inherent disadvantage of not learning from unacceptable plans. …”
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YOLOv8n-SMMP: A Lightweight YOLO Forest Fire Detection Model by Nianzu Zhou, Demin Gao, Zhengli Zhu

“…Global warming has driven a marked increase in forest fire occurrences, underscoring the critical need for timely and accurate detection to mitigate fire-related losses. Existing forest fire detection algorithms face limitations in capturing flame and smoke features in complex natural environments, coupled with high computational complexity and inadequate lightweight design for practical deployment. …”
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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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Adaptive Fusion of LiDAR Features for 3D Object Detection in Autonomous Driving by Mingrui Wang, Dongjie Li, Josep R. Casas, Javier Ruiz-Hidalgo

“…However, traditional early or late fusion methods face challenges such as high bandwidth and computational resources, which make it difficult to balance data transmission efficiency with the accuracy of perception of the surrounding environment, especially for the detection of smaller objects such as pedestrians. …”
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Research on the lightweight detection method of rail internal damage based on improved YOLOv8 by Xiaochun Wu, Shuzhan Yu

“…This model offers efficient and reliable technical support for detecting and classifying internal rail damage in rail flaw detection tasks. …”
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Real-Time Runway Detection Using Dual-Modal Fusion of Visible and Infrared Data by Lichun Yang, Jianghao Wu, Hongguang Li, Chunlei Liu, Shize Wei

“…This study proposes a salient object detection (SOD) method that integrates visible and infrared sensors for robust airport runway detection in complex environments. …”
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Low resolution remote sensing object detection with fine grained enhancement and swin transformer by Zhijing Xu, Xin Wang, Kan Huang, Ren Chen

“…Abstract Object detection in remote sensing images is a highly complex and challenging task. …”
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Multi-Level Intertemporal Attention-Guided Network for Change Detection in Remote Sensing Images by Shuo Liu, Qinyu Zhang, Yuhang Zhang, Xiaochen Niu, Wuxia Zhang, Fei Xie

“…These methods focus on regions of interest, improving detection accuracy and efficiency. However, external factors can introduce many pseudo-changes, presenting significant challenges for CD. …”
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RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR by Hao Zhang, Zheng Ma, Xiang Li

“…Recently, deep-learning-based object detection has made significant progress. However, due to large variations in target scale, the predominance of small targets, and complex backgrounds in remote sensing imagery, remote sensing object detection still faces challenges, including low detection accuracy, poor real-time performance, high missed detection rates, and high false detection rates in practical applications. …”
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YOLOv11-GSF: an optimized deep learning model for strawberry ripeness detection in agriculture by Haoran Ma, Qian Zhao, Runqing Zhang, Chunxu Hao, Wenhui Dong, Xiaoying Zhang, Fuzhong Li, Xiaoqin Xue, Gongqing Sun

“…The challenge of efficiently detecting ripe and unripe strawberries in complex environments like greenhouses, marked by dense clusters of strawberries, frequent occlusions, overlaps, and fluctuating lighting conditions, presents significant hurdles for existing detection methodologies. …”
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An adaptive neuro-fuzzy inference scheme for defect detection and classification of solar PV cells by Ranganai Tawanda Moyo, Mendon Dewa, Héctor Felipe Mateo Romero, Victor Alonso Gómez, Jose Ignacio Morales Aragonés, Luis Hernández-Callejo

“…Traditional defect detection and classification methods often face challenges in providing precise and adaptable solutions to this complex problem. …”
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A lightweight fabric defect detection with parallel dilated convolution and dual attention mechanism by Zheqing Zhang, Kezhong Lu, Gaoming Yang

“…To increase detection efficiency, a variety of automatic fabric defect detections have been developed. …”
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Automated Antithrombin Activity Detection with Whole Capillary Blood Based on Digital Microfluidic Platform by Dongshuo Li, Hanqi Hu, Hanzhi Zhang, Lei Shang, Tao Zhao, Qingchen Zhao, Shuhao Zhang, Fucun Ma, Guowei Liang, Rongxin Fu, Xuekai Liu

“…However, traditional detection methods often require large sample volumes, complex procedures, and lengthy processing times. …”
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Detection of Citrus Huanglongbing in Natural Field Conditions Using an Enhanced YOLO11 Framework by Liang Cao, Wei Xiao, Zeng Hu, Xiangli Li, Zhongzhen Wu

“…We constructed a multi-symptom HLB leaf dataset (MS-HLBD) containing 9219 annotated images across five classes: Healthy (1862), HLB blotchy mottling (2040), HLB Zinc deficiency (1988), HLB yellowing (1768), and Canker (1561), collected under diverse field conditions. To improve detection performance, the DCH-YOLO11 framework incorporates three novel modules: the C3k2 Dynamic Feature Fusion (C3k2_DFF) module, which enhances early and subtle lesion detection through dynamic feature fusion; the C2PSA Context Anchor Attention (C2PSA_CAA) module, which leverages context anchor attention to strengthen feature extraction in complex vein regions; and the High-efficiency Dynamic Feature Pyramid Network (HDFPN) module, which optimizes multi-scale feature interaction to boost detection accuracy across different object sizes. …”
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Profiling low-mRNA content cells in complex human tissues using BD Rhapsody single-cell analysis by Alexandra Scheiber, Manuel Trebo, Annabella Pittl, Isabel Heidegger, Theresa Hautz, Rupert Oberhuber, Zlatko Trajanoski, Florian Augustin, Sieghart Sopper, Dominik Wolf, Andreas Pircher, Stefan Salcher

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YOLO-PWSL-Enhanced Robotic Fish: An Integrated Object Detection System for Underwater Monitoring by Lingrui Lei, Ying Tang, Weidong Zhang, Quan Tang, Haichi Hao

“…In fact, we designed a multilevel attention fusion block (LGFB) that enhances perception in complex scenarios, to optimize the accuracy of the detected frames, the Wise-ShapeIoU loss function was used, and in order to reduce the parameters and FLOPs of the model, a lightweight convolution method called PConv was introduced. …”
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