Accurate Detection and Tracking of Small-Scale Vehicles in High-Altitude Unmanned Aerial Vehicle Bird-View Imagery by Heshan Zhang, Xin Tan, Mengwei Fan, Cunshu Pan, Zhanji Zheng, Shuang Luo, Jin Xu

“…Furthermore, an image segmentation method is proposed to effectively reduce missed and false detection events. It divides the original high-definition image into multiple subimages, first detected and then reassembled. …”
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DMN-YOLO: A Robust YOLOv11 Model for Detecting Apple Leaf Diseases in Complex Field Conditions by Lijun Gao, Hongwu Cao, Hua Zou, Huanhuan Wu

“…These enhancements effectively reduce both missed and false detections. Additionally, a normalized Wasserstein distance (NWD) loss function is introduced to mitigate localization errors, particularly for small or overlapping lesions. …”
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Dilated Convolution and YOLOv8 Feature Extraction Network: An Improved Method for MRI-Based Brain Tumor Detection by Lincy Annet Abraham, Gopinath Palanisamy, Veerapu Goutham

“…In addition, the DC-YOLOv8FEN uses a position regression loss function and adds Wise-IoU’s dynamic non-monotonic focus mechanism to make it even better. …”
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Novel Spectrum Inversion-Based Double-Sideband Modulation with Low Complexity for a Self-Coherent Detection System by Peng Qin, Jiahao Huo, Haolin Bai, Xiaoying Zhang, Jianlong Tao, Keping Long

“…However, DSB-SCD is fundamentally constrained by the non-ideal receiver transfer function, necessitating a guard band between the carrier and signal. …”
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SDDGRNets: Level–Level Semantically Decomposed Dynamic Graph Reasoning Network for Remote Sensing Semantic Change Detection by Zhuli Xie, Gang Wan, Yunxia Yin, Guangde Sun, Dongdong Bu

“…In addition, the edge constraint module is used to constrain boundary pixels and reduce semantic ambiguity. The weighted loss function supervises and optimizes each module separately to enable the network to acquire the optimal feature representation. …”
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KronNet a lightweight Kronecker enhanced feed forward neural network for efficient IoT intrusion detection by Saeed Ullah, Junsheng Wu, Mian Muhammad Kamal, Abdul Khader Jilani Saudagar

“…This study introduces KronNet, a lightweight feed-forward neural network enhanced with Kronecker product operations, designed for real-time IoT intrusion detection. KronNet leverages Gaussian Mixture Model (GMM)-based oversampling and a hybrid loss function combining Focal Loss and Cross-Entropy with adaptive class weighting to address class imbalance, ensuring robust detection across diverse attack types. …”
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SPDC-YOLO: An Efficient Small Target Detection Network Based on Improved YOLOv8 for Drone Aerial Image by Jingxin Bi, Keda Li, Xiangyue Zheng, Gang Zhang, Tao Lei

“…Target detection in UAV images is of great significance in fields such as traffic safety, emergency rescue, and environmental monitoring. …”
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Optimization of a direct-detection UV wind lidar architecture for 3D wind reconstruction at high altitude by T. Boulant, T. Michel, M. Valla

“…The optimization involves selecting an emission–reception telescope to maximize the number of collected photons backscattered between 100 and 300 <span class="inline-formula">m</span>, a background filter to reduce noise from the scene, and photomultiplier tubes (PMTs) to minimize detection noise. …”
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TMBO-AOD: Transparent Mask Background Optimization for Accurate Object Detection in Large-Scale Remote-Sensing Images by Tianyi Fu, Hongbin Dong, Benyi Yang, Baosong Deng

“…When implemented with YOLOv5 (anchor based), the framework reduces the candidate generation time by 48.36%, while the YOLOv8 (anchor-free) implementation achieves a 46.81% reduction, both with maintained detection accuracy.…”
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A Lightweight YOLOv8s Algorithm for Ceiling Fan Blade Defect Detection With Optimized Pruning and Knowledge Distillation by Qinyuan Huang, Chen Fan, Yuqi Sun, Jiaxiong Huang, Wengziyang Jiang

“…While industrial ceiling fans play a vital role in maintaining efficient airflow and system stability within factory settings, surface defects on their blades that arise over time can disrupt their functionality and create safety hazards. Detecting these surface defects is crucial; however, accurate and rapid detection typically involves complex machine vision algorithms, such as You Only Look Once (YOLO) networks, that require considerable computing resources, which contradicts the industry&#x2019;s preference for simpler algorithms that can be deployed using low-cost computing power. …”
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LD-Det: Lightweight Ship Target Detection Method in SAR Images via Dual Domain Feature Fusion by Hang Yu, Bingzong Liu, Lei Wang, Teng Li

“…Additionally, we propose a hybrid IoU loss function specifically designed to enhance the detection of small objects, improving localization accuracy and robustness. …”
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Rapid and accurate detection of peanut pod appearance quality based on lightweight and improved YOLOv5_SSE model by Zhixia Liu, Xilin Zhong, Chunyu Wang, Guozhen Wu, Fengyu He, Jing Wang, Dexu Yang

“…Furthermore, the substitution of various loss functions was investigated, with the Focal-EIoU loss function employed as the regression loss term for predicting bounding boxes, thereby improving inference accuracy.ResultsCompared to the YOLOv5s network model, SSE-YOLOv5s boasts a mere 6.7% of the original model’s parameters, 7.8% of the computation, and an FPS rate 115. 1% higher. …”
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Fresh Tea Leaf-Grading Detection: An Improved YOLOv8 Neural Network Model Utilizing Deep Learning by Zejun Wang, Yuxin Xia, Houqiao Wang, Xiaohui Liu, Raoqiong Che, Xiaoxue Guo, Hongxu Li, Shihao Zhang, Baijuan Wang

“…This approach integrates a Hierarchical Vision Transformer using Shifted Windows to replace segments of the original YOLOv8’s network architecture, thereby alleviating the computational load of dense image processing tasks and reducing computational expenses. The incorporation of an Efficient Multi-Scale Attention Module with Cross-Spatial Learning serves to attenuate the influence of irrelevant features in complex backgrounds, which in turn, elevates the model’s detection Precision. …”
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Vowel classification with combining pitch detection and one‐dimensional convolutional neural network based classifier for gender identification by Chia‐Hung Lin, Hsiang‐Yueh Lai, Ping‐Tzan Huang, Pi‐Yun Chen, Chien‐Ming Li

“…Hence, an IVR‐based system dictates that ASR should function through users' voices (specific voice‐frequency bands) to identify customers' age and gender and interact with a host system. …”
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Functional mechanism and clinical implications of lncRNA NEAT1 in traumatic fractures and their correlation with delayed healing by Hua Meng, Zhaoyu Chen, Meng Han, Qianqian Cheng, Yanqi Shang, Hongqing Wang, Shenyi Lu

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Highly sensitive, responsive, and selective iodine gas sensor fabricated using AgI-functionalized graphene by Zhuo Chen, Qiong Lei, Yinchang Ma, Jinrong Wang, Yuan Yan, Jun Yin, Jiaqiang Li, Jie Shen, Guanxing Li, Tingting Pan, Xinglong Dong, Bambar Davaasuren, Yaping Zhang, Jefferson Zhe Liu, Jun Tao, Yu Han

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A deep learning-based detection model and illumination-adaptive behavioral analysis for soldier crabs in the intertidal zone by Liangjun Li, Zhihao Ren, Cheng Tang, Shengning Lu, Yong Liang

“…However, due to their small size and tidal-driven activity patterns, conventional behavior detection methods suffer from low efficiency and considerable observer bias, particularly under dark conditions where detection errors and omissions are prevalent. …”
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BESW-YOLO: A Lightweight SAR Image Detection Model Based on YOLOv8n for Complex Scenarios by Xiao Tang, Kun Cao, Yunzhi Xia, Enkun Cui, Weining Zhao, Qiong Chen

“…However, traditional detection methods often suffer from false alarms and missed detections. …”
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A Cross-Stage Focused Small Object Detection Network for Unmanned Aerial Vehicle Assisted Maritime Applications by Gege Ding, Jiayue Liu, Dongsheng Li, Xiaming Fu, Yucheng Zhou, Mingrui Zhang, Wantong Li, Yanjuan Wang, Chunxu Li, Xiongfei Geng

“…Then, structural pruning was applied to refine the model and enhance its capability in detecting small objects. Moreover, to conserve computational resources, a lightweight CED module was introduced to reduce parameters and conserve the computing resources of the UAV. …”
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Cardiac arrhythmias in pulmonary hypertension: A review of mechanisms, prevalence, clinical significance, and patient functional capacity by Adam Rafałowicz, Urszula, Justyna Wojciechowska, Jakub Mierzejewski, Bartosz Łuniewski, Angelika Macko, Monika Olszańska, Kamil, Klaudiusz Lauko, Aleksandra Sokół, Klaudia Mościszko, Dominika Hakało

“…Conclusions: Early detection and maintenance of sinus rhythm enhance survival and functional capacity, highlighting the necessity of research and interventions directed at atrial remodelling, neurohormonal modulation and myocardial ischaemia. …”
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