Autonomous UAV Detection of <i>Ochotona curzoniae</i> Burrows with Enhanced YOLOv11 by Huimin Zhao, Linqi Jia, Yuankai Wang, Fei Yan

“…By combining the lightweight visual Transformer architecture EfficientViT with the hybrid attention mechanism CBAM, we develop an enhanced YOLOv11-AEIT algorithm: (1) EfficientViT is employed as the backbone network, strengthening micro-burrow feature representation through a multi-scale feature coupling mechanism that alternates between local window attention and global dilated attention; (2) the integration of CBAM (Convolutional Block Attention Module) in the feature fusion neck reduces false detections through dual-channel spatial attention filtering. …”
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CB-MTE: Social Bot Detection via Multi-Source Heterogeneous Feature Fusion by Meng Cheng, Yuzhi Xiao, Tao Huang, Chao Lei, Chuang Zhang

“…Social bots increasingly mimic real users and collaborate in large-scale influence campaigns, distorting public perception and making their detection both critical and challenging. Traditional bot detection methods, constrained by single-source features, often fail to capture the complete behavioral and contextual characteristics of social bots, especially their dynamic behavioral evolution and group coordination tactics, resulting in feature incompleteness and reduced detection performance. …”
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Integrating SAM priors with U-Net for enhanced multiclass cell detection in digital pathology by Zheng Wu, Ji-Yun Yang, Chang-Bao Yan, Cheng-Gui Zhang, Hai-Chao Yang

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Multi-object detection for underground unmanned locomotives based on DYCS-YOLOv8n by XU Jinhui, WANG Wenshan, WANG Shuang, WANG Wenyue, ZHAO Tingting

“…Experiments conducted on a self-constructed underground unmanned locomotive dataset showed that: ① The DYCS-YOLOv8n model achieved a mean Average Precision (mAP@0.5) of 97.5%, an improvement of 3.4% over the YOLOv8n model, with a detection speed of 46.35 frames per second, meeting the requirements for real-time detection. ② Compared with mainstream YOLO series object detection models, DYCS-YOLOv8n achieved the optimal mAP@0.5, maintaining a lightweight structure while ensuring high computational speed. ③ In complex underground scenarios with noise and low illumination, the DYCS-YOLOv8n model exhibited high average detection confidence for pedestrians, tracks, and signal lights, with no cases of missed or false detections.…”
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A Contrast-Enhanced Approach for Aerial Moving Target Detection Based on Distributed Satellites by Yu Li, Hansheng Su, Jinming Chen, Weiwei Wang, Yingbin Wang, Chongdi Duan, Anhong Chen

“…This study proposes a novel technique for detecting aerial moving targets using multiple satellite radars. …”
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Detection of the distribution frequency of Diego blood type based on multiplex droplet digital PCR by Xiaoyue Chu, Jin Wang, Dazhou Wu, Juan Mao, Zhicheng Zhang, Hua Xu, Chaofeng Ma

“…These findings are valuable for understanding genetic polymorphisms and heterogeneity in the local population. In this study, multiplex digital PCR was employed for the first time to detect the Diego blood group. …”
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An efficient bearing fault detection strategy based on a hybrid machine learning technique by Khalid Alqunun, Mohammed Bachir Bechiri, Mohamed Naoui, Abderrahmane Khechekhouche, Ismail Marouani, Tawfik Guesmi, Badr M. Alshammari, Amer AlGhadhban, Abderrahim Allal

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Multi-strategy enhanced marine predator algorithm: performance investigation and application in intrusion detection by Zhongmin Wang, Yujun Zhang, Jun Yu, YuanYuan Gao, Guangwei Zhao, Essam H. Houssein, Rui Zhong

“…Finally, we extend the proposed MEMPA to optimize the hyper-parameters of the Extreme Learning Machine (ELM) for intrusion detection, and the performance of MEMPA-ELM increases the average accuracy by 0.79% than the second-best model. …”
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Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection by Ghufran Basim Alghanimi, Hadeel Aljobouri, Khaleel Akeash Alshimmari, Rasha Massoud

“…The combination of deep learning and FS techniques in this research produces an interesting diagnostic framework that can potentially increase efficiency and accuracy in thyroid cancer detection, especially in local healthcare centers. …”
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Deep and hybrid learning of MRI diagnosis for early detection of the progression stages in Alzheimer’s disease by Ibrahim Abunadi

“…The first proposed system is to classify a data set using artificial neural networks (ANNs) and feed-forward neural networks (FFNN) based on the features extracted in a hybrid manner by using a combination of Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT), and Gray Level Co-occurrence Matrix (GLCM) algorithms. …”
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Enhanced Object Detection in Thangka Images Using Gabor, Wavelet, and Color Feature Fusion by Yukai Xian, Yurui Lee, Te Shen, Ping Lan, Qijun Zhao, Liang Yan

“…Existing detectors often struggle to capture both global structures and local details and rarely leverage domain-specific visual priors. …”
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DSF-YOLO for weld defect detection in X-ray images with dynamic staged fusion by Meng Zhang, Yanzhu Hu, Binbin Xu, Lisha Luo, Song Wang

“…Abstract Weld defect detection poses significant challenges including ambiguous boundaries, diverse defect shapes, and the requirement for precise localization. …”
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A Spatio-Temporal Attention Network With Multiframe Information for Infrared Small Target Detection by Donghui Liu, Wenlong Zhang, Zicheng Feng, Xiaoliang Sun, Rui Zhang, Yang Shang

“…Infrared small target detection holds great potential for various applications, but also faces numerous challenges. …”
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Bi-temporal Gaussian feature dependency guided change detection in remote sensing images by Yi Xiao, Bin Luo, Jun Liu, Xin Su, Wei Wang

“…Subsequently, we have designed a novel detail feature compensation (DFC) module, which compensates for detail feature loss and contamination introduced during the upsampling process from the perspectives of enhancing local features and refining global features. The BGFD has effectively reduced pseudo-changes and enhanced the detection capability of detail information. …”
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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

“…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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Adaptive Granularity-Fused Keypoint Detection for 6D Pose Estimation of Space Targets by Xu Gu, Xi Yang, Hong Liu, Dong Yang

“…The accuracy of this process hinges critically on the initial keypoint detection, which is currently limited by predominantly singular-scale detection techniques and fails to exploit sufficient information. …”
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Protocol for tyramide signal amplification immunohistochemical detection of Notch1 signaling in the vascular system by Ying Lin, Shekhar Singh, Chong Xu, Zeyu Wang, Cailin Feng, Dongyang Jiang, Lingfeng Luo, Weiming Li, Wenliang Che, Guofu Zhu

“…Here, we present a protocol for tyramide signal amplification (TSA) immunohistochemistry, tailored explicitly for detecting Notch signaling components in vascular tissues. …”
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Large indel detection in region-based phased diploid assemblies from linked-reads by Can Luo, Brock A. Peters, Xin Maizie Zhou

“…Finally, it detects variants in the contigs through a pairwise contig-to-reference comparison. …”
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YOLOv8s-Longan: a lightweight detection method for the longan fruit-picking UAV by Jun Li, Jun Li, Jun Li, Kaixuan Wu, Meiqi Zhang, Hengxu Chen, Hengyi Lin, Yuju Mai, Linlin Shi

“…To make the network lightweight and improve its generalization performance, the Average and Max pooling attention (AMA) attention module is designed and integrated into the DenseAMA and C2f-Faster-AMA modules on the proposed backbone network. To improve the detection accuracy, a crossstage local network structure VOVGSCSPC module is designed, which can help the model better understand the information of the image through multiscale feature fusion and improve the perception and expression ability of the model. …”
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Automated detection and identification of white-backed planthoppers in paddy fields using image processing by Qing YAO, Guo-te CHEN, Zheng WANG, Chao ZHANG, Bao-jun YANG, Jian TANG

“…In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient (HOG) features and a support vector machine (SVM) classifier that was trained on Gabor and Local Binary Pattern (LBP) features to detect WBPHs and remove impurities. …”
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