A Tactical Conflict Detection and Resolution Method for En Route Conflicts in Trajectory-Based Operations by Dong Sui, Kai Zhang

“…In the conflict detection (CD) submodule, a spatial data structure with low time complexity, the R tree algorithm, is used. …”
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Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection by Zirou Jiang, Jintao Fu, Tianchen Zeng, Renjie Liu, Peng Cong, Jichen Miao, Yuewen Sun

“…Additionally, we introduce a custom backbone, edge-prior net (EP-Net), to capture features across multiple spatial scales, enhancing the recognition of subtle and complex defect patterns. During inference, the multi-branch structure is consolidated into a single-branch equivalent to accelerate detection without compromising accuracy. …”
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EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR by Jiajun Dun, Hai Yang, Shixin Yuan, Ying Tang

“…In the context of the rapid popularization of clean energy, the precise identification of surface defects on photovoltaic modules has become a core technical bottleneck limiting the operational efficiency of power stations. In response to the shortcomings of existing detection methods in identifying tiny defects and model efficiency, this study innovatively constructed the EER-DETR detection framework: firstly, a feature reconstruction module WDBB with a differentiable branch structure was introduced to significantly enhance the feature retention ability for fine cracks and other small targets; secondly, an adaptive feature pyramid network EHFPN was innovatively designed, which achieved efficient integration of multi-level features through a dynamic weight allocation mechanism, reducing the model complexity by 9.7% while maintaining detection accuracy, solving the industry problem of “precision—efficiency imbalance” in traditional feature pyramid networks; finally, an enhanced upsampling component was introduced to effectively address the problem of detail loss that occurs in traditional methods during image resolution enhancement. …”
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A Low-Friction Capsule Robot with Drive–Control–Sensing Integration for Gastrointestinal Lesion Detection by Ziying Wang, Hang Yin, Zilin Wei, Shisheng Chen, Jiameng Li, Jingyao Sun, Wei Zhao, Yejuan Jia

“…Here, a capsule robot with a low coefficient of friction is proposed to continuously detect pH and temperature. …”
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High resolution remote sensing image object detection algorithm based on improved YOLOv8 by ZHANG Xia, QIAO Huanyu, CAO Feng

“…Bi-directional feature pyramid network and semantics and detail infusion module were combined as the algorithm's feature fusion network. An improved efficient decoupled detection head was designed. Finally, combining NWD and WIoU loss functions, a new loss function NWD-WIoU was designed to accelerate the convergence speed of algorithm detection and improve the detection performance of the algorithm for small objects. …”
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Liveness Detection in Computer Vision: Transformer-Based Self-Supervised Learning for Face Anti-Spoofing by Arman Keresh, Pakizar Shamoi

“…We compared the performance of the proposed fine-tuned ViT model using the DINO framework against traditional models, including CNN Model EfficientNet b2, EfficientNet b2 (Noisy Student), and Mobile ViT on the face anti-spoofing task. …”
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A method for detecting abnormal electricity prices at charging stations based on two rounds of clustering by XUE Xiaohui, ZHANG Wen, ZHANG Jing, CHEN Yan, ZHOU Chun, CHEN Liang, CAO Zengwei

“…This not only damages the fairness and efficiency of the electricity market but also affects the economic benefits of power enterprises and the electricity costs of users. …”
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An MSRE-Assisted Glycerol-Enhanced RPA-CRISPR/Cas12a Method for Methylation Detection by Zhiquan Lu, Zilu Ye, Ping Li, Yike Jiang, Sanyang Han, Lan Ma

“…Conventional methylation detection methods relying on bisulfite conversion have limitations such as time-consuming, complex processes and sample degradation; thus, a more rapid and efficient method is needed. …”
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Feature Multi-Scale Enhancement and Adaptive Dynamic Fusion Network for Infrared Small Target Detection by Zenghui Xiong, Zhiqiang Sheng, Yao Mao

“…This study aims to address a series of challenges in infrared small target detection, particularly in complex backgrounds and high-noise environments. …”
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A Multimodal Parallel Transformer Framework for Apple Disease Detection and Severity Classification with Lightweight Optimization by Chuhuang Zhou, Xinjin Ge, Yihe Chang, Mingfei Wang, Zhongtian Shi, Mengxue Ji, Tianxing Wu, Chunli Lv

“…To address the impact of complex disease characteristics and diverse environmental factors on detection accuracy, this study proposes a multimodal parallel transformer-based approach for apple disease detection and classification. …”
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A Hybrid Graph-Based Risk Assessment and Attack Path Detection Model for IoT Systems by Ferhat Arat, Sedat Akleylek, Zaliha Yuce Tok

“…Our proposed approach targets high-vulnerability components within the system through risk-weighted backtracking, thereby facilitating more efficient attack path detection and filtering. The developed method is evaluated in a border security case scenario, with comparisons made in terms of algorithmic and asymptotic complexity. …”
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Automatic Detection of Landslide Surface Cracks from UAV Images Using Improved U-Network by Hao Xu, Li Wang, Bao Shu, Qin Zhang, Xinrui Li

“…Surface cracks are key indicators of landslide deformation, crucial for early landslide identification and deformation pattern analysis. However, due to the complex terrain and landslide extent, manual surveys or traditional digital image processing often face challenges with efficiency, precision, and interference susceptibility in detecting these cracks. …”
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Enhanced Cloud Detection Using a Unified Multimodal Data Fusion Approach in Remote Images by Yan Mo, Puhui Chen, Wanting Zhou, Wei Chen

“…Aiming at the complexity of network architecture design and the low computational efficiency caused by variations in the number of modalities in multimodal cloud detection tasks, this paper proposes an efficient and unified multimodal cloud detection model, M2Cloud, which can process any number of modal data. …”
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Grounding DINO and distillation-enhanced model for advanced traffic sign detection and classification in autonomous vehicles by Hoang Ngoc Tran, Nam Nhat Ngo Nguyen, Nhi Quynh Phan Le, Thu Anh Ngoc Le, Anh Duy Nguyen

“…The self-distilled network further reduces model size by tenfold compared to baseline models (e.g., MobileNetV2, VGG16, ResNet18), yet maintains competitive accuracy, providing a robust, resource-efficient solution for real-time deployment. Our results indicate that integrating semantic grounding with distillation-based compression not only enhances traffic sign detection performance but also delivers a scalable and efficient approach for complex traffic environments. …”
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Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning Methods by Huibao Yang, Bangshuai Li, Xiujing Gao, Bo Xiao, Hongwu Huang

“…To overcome the limitations of the residual chi-square method in detecting gradual faults, a cumulative residual detection approach with error coefficient amplification was proposed. …”
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FAMHE-Net: Multi-Scale Feature Augmentation and Mixture of Heterogeneous Experts for Oriented Object Detection by Yixin Chen, Weilai Jiang, Yaonan Wang

“…These results highlight the effectiveness of FAMEH-Net in object detection within complex remote sensing images.…”
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Financial fraud detection using a hybrid deep belief network and quantum optimization approach by Gui Yu, Zhenlin Luo

“…The proposed model employs a hybrid model optimisation strategy that integrates convolutional neural networks (CNNs), long short-term memory networks (LSTMs) and graph neural networks (GNNs).Conventional detection methods depend on manual rules and statistical analyses, which are inadequate for handling large-scale, high-density and complex financial market data. …”
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A Lightweight Algorithm for Detection and Grading of Olive Ripeness Based on Improved YOLOv11n by Fengwu Zhu, Suyu Wang, Min Liu, Weijie Wang, Weizhi Feng

“…The proposed model exhibits significant advantages in terms of lightweight design and improved detection efficiency, demonstrating substantial potential for practical deployment. …”
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BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n by Shengnan Hao, Erjian Gao, Zhanlin Ji, Ivan Ganchev

“…Conducted experiments show that BCS_YOLO achieves 78.4%, 73.7%, 76.0%, and 82.0% in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></semantics></math></inline-formula>, <i>recall</i>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mn>1</mn><mtext> </mtext><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e</mi></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><mi>P</mi><mo>@</mo><mn>50</mn></mrow></semantics></math></inline-formula>, respectively, representing corresponding improvements of 3.0%, 3.3%, 3.2%, and 4.6% compared to the baseline model (YOLOv11n), while also outperforming the mainstream object detection models. In summary, the proposed BCS_YOLO model provides a practical and scalable solution for efficient detection of corn leaf diseases and pests in complex smart-agriculture scenarios, demonstrating significant theoretical and application value.…”
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MEP-YOLOv5s: Small-Target Detection Model for Unmanned Aerial Vehicle-Captured Images by Shengbang Zhou, Song Zhang, Chuanqi Li, Shutian Liu, Dong Chen

“…Due to complex backgrounds, significant scale variations of targets, and dense distributions of small objects in Unmanned Aerial Vehicle (UAV) aerial images, traditional object detection algorithms face challenges in adapting to such scenarios. …”
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