Insulator Defect Detection Algorithm Based on Improved YOLOv11n by Junmei Zhao, Shangxiao Miao, Rui Kang, Longkun Cao, Liping Zhang, Yifeng Ren

“…Key innovations include a redesigned C3k2 module that incorporates multidimensional dynamic convolutions (ODConv) for improved feature extraction, the introduction of Slimneck to reduce model complexity and computational cost, and the application of the WIoU loss function to optimize anchor box handling and to accelerate convergence. …”
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Detecting Mentions of Green Practices in Social Media Based on Text Classification by Anna Valerevna Glazkova, Olga Vladimirovna Zakharova, Anton Viktorovich Zakharov, Natalya Nikolayevna Moskvina, Timur Ruslanovich Enikeev, Arseniy Nikolaevich Hodyrev, Vsevolod Konstantinovich Borovinskiy, Irina Nikolayevna Pupysheva

“…Conversational RuBERT model was chosen for the implementation of the application prototype. The main function of the prototype is to detect the presence of the mention of nine types of green practices in the text. …”
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Integrated Communication and RIS-Aided Track-Before-Detect Radar Sensing by Georgios Mylonopoulos, Luca Venturino, Emanuele Grossi, Stefano Buzzi, Ciro D'Elia

“…Our results show that, by increasing the number of scans processed by the radar detector (and therefore its implementation complexity), we can reduce the amount of power dedicated to the radar function while maintaining the same sensing performance (measured in terms of probability of target detection and root mean square error in the estimation of target position); this excess power can be reused to increase the user sum-rate.…”
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Optimization Methodology for Meningioma and Acoustic Neuroma Detection Model Based on DCGAN by CHEN Jingcong, RAN Fengwei, ZHANG Haowei, LIU Ying

“…Establishing an automatic tumor detection model using deep learning methods can effectively reduce the subjectivity of manual diagnosis, decrease missed diagnosis rates, and improve work efficiency. …”
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SR-DETR: Target Detection in Maritime Rescue from UAV Imagery by Yuling Liu, Yan Wei

“…By refining spatial attention mechanisms, the module significantly boosts cross-scale target recognition capabilities in the model, especially offering advantages for detecting smaller objects. To improve localization precision, we develop a novel loss function for bounding box regression, named Focaler-GIoU, which performs particularly well when handling densely packed and small-scale objects. …”
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Convolutional Edge Constraint-Based U-Net for Salient Object Detection by Le Han, Xuelong Li, Yongsheng Dong

“…The salient object detection is receiving more and more attention from researchers. …”
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Detection of Access Point Spoofing in the Wi-Fi Fingerprinting Based Positioning by Juraj Machaj, Clément Safon, Slavomír Matúška, Peter Brída

“…Based on the achieved results proposed SFKNN provided good detection of the spoofing and helped to reduce the mean localization error by 2–5 m, especially when the number of spoofed access points was higher.…”
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Damage Detection Method for Road Ancillary Facilities Integrating Attention Mechanism by Shuang Yang, Huiqin Wang, Ke Wang, Nan Guo

“…The model first introduces the D-GhostNet V3Conv module, replacing the standard convolutional layers, significantly enhancing feature extraction capabilities while reducing computational costs. Additionally, the improved AR-BiFormer attention mechanism is integrated into the backbone network, enabling adaptive weight adjustment of feature maps through parallel processing of contextual information, thereby effectively improving the detection of small targets in complex scenes. …”
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Enhanced Lightweight YOLO Model for Efficient Vehicle Detection in Satellite Imagery by Mohamad Haniff Junos, Anis Salwa Mohd Khairuddin, Elmi Abu Bakar, Ahmad Faizul Hawary

“…Finally, the proposed model incorporates the Swish function and an extra YOLO head for detection. The experimental results evaluated on the VEDAI dataset demonstrated that the proposed model achieved a higher mean average precision value and generated the smallest model size compared to the other lightweight models. …”
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SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments by Quan Wang, Ye Hua, Qiongdan Lou, Xi Kan

“…Experiments on greenhouse tomato data sets demonstrate that SWMD-YOLO achieves 93.47% mAP50 with a detection speed of 75.68 FPS, outperforming baseline models in accuracy while reducing parameters by 18.9%. …”
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Dual Transformers With Latent Amplification for Multivariate Time Series Anomaly Detection by Yeji Choi, Kwanghoon Sohn, Ig-Jae Kim

“…Third, we refine the anomaly scoring process using a scaled-softmax function, which balances relative and absolute deviations to reduce softmax-induced bias. …”
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An Efficient Printing Defect Detection Based on YOLOv5-DCN-LSK by Jie Liu, Zelong Cai, Kuanfang He, Chengqiang Huang, Xianxin Lin, Zhenyong Liu, Zhicong Li, Minsheng Chen

“…Finally, we apply model pruning techniques to reduce the model’s size and parameter count, thereby achieving faster detection. …”
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Kans-Unet Model and Its Application in Image Patch-Shaped Detection by Xingsu Li, Zhong Li, Jianping Huang, Ying Han, Kexin Zhu, Bo Hao, Junjie Song, Yumeng Huo

“…The module applies a learnable activation function at the edge of the network, which not only reduces the number of model parameters but also significantly improves the generalization performance of the network. …”
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Detecting faults in electronic combustion engine control systems by acoustic parameters by V. D. Gabidulin, V. N. Dobromirov

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Copper Nodule Defect Detection in Industrial Processes Using Deep Learning by Zhicong Zhang, Xiaodong Huang, Dandan Wei, Qiqi Chang, Jinping Liu, Qingxiu Jing

“…The surface of cathodic copper plates is often affected by various electrolytic process factors, resulting in the formation of nodule defects that significantly impact surface quality and disrupt the downstream production process, making the prompt detection of these defects essential. At present, the detection of cathode copper plate nodules is performed by manual identification. …”
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Cardiovascular Disease Detection through Innovative Imbalanced Learning and AUC Optimization by Karthikeyan Palanisamy, Krishnaveni Krishnasamy, Praba Venkadasamy

“…In this paper, we introduce a novel imbalanced learning approach named Imbalanced Maximizing-Area Under the Curve (AUC) Proximal Support Vector Machine (ImAUC-PSVM), which harnesses the foundational principles of traditional PSVM for the detection of CVDs. The ImAUC-PSVM method offers several key advantages: 1) It skillfully incorporates AUC maximization directly into the objective function. …”
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Sonar-based object detection for autonomous underwater vehicles in marine environments by Zhen Wang, Zhen Wang, Jianxin Guo, Shanwen Zhang, Yucheng Zhang

“…Additionally, the CIoU-DFL loss optimization function was constructed to address the class imbalance in sonar data and reduce model computational complexity. …”
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Lightweight and robust ship detection method driven by self-attention mechanism by Feng MA, Zihui SHI, Jie SUN, Chen CHEN, Xianbin MAO, Xinping YAN

“…Moreover, the method employs the SCYLLA-IoU (SIoU) loss function to constrain the detection heads, thereby reducing regression freedom and improving detection accuracy and robustness. …”
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DEM-based topographic change detection considering the spatial distribution of errors by Yufeng He, Shaohua Lei, Wen Dai, Xi Chen, Bo Wang, Yehua Sheng, Hui Lin

“…In particular, the use of level of detection can effectively reduce the misclassification of erosion or deposition in stable topography areas. …”
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YOLO-SAATD: An efficient SAR airport and aircraft target detector by Daobin Ma, Zhanhong Lu, Zixuan Dai, Yangyue Wei, Li Yang, Haimiao Hu, Wenqiao Zhang, Dongping Zhang

“…Precision: Wise-IoU loss function is used to optimize bounding box localization and enhance detection accuracy. …”
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