Vehicle detection and classification for traffic management and autonomous systems using YOLOv10 by Anning Ji, Xintao Ma

“…Our approach leverages the advantages of each method to enhance detection accuracy and efficiency, especially in complex traffic scenarios. …”
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Multimodal image fusion for ich detection and classification using parallel Dl models by Sri Sangepu Nagaraju, S. Prince Mary, V. Pavani Chandra, Nandam Gayatri

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Research on foreign object intrusion detection in railway tracks based on MSL-YOLO by Hongxia Niu, Dingchao Feng, Tao Hou

“…This integration improves multi-scale feature representation and model efficiency. In addition, a Lightweight Shared Convolutional Detection Head (LSCD) is employed to replace the original head, reducing complexity while maintaining detection accuracy. …”
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YOLO-HVS: Infrared Small Target Detection Inspired by the Human Visual System by Xiaoge Wang, Yunlong Sheng, Qun Hao, Haiyuan Hou, Suzhen Nie

“…The experimental results demonstrate that the proposed approach exhibits enhanced robustness in detecting targets under severe occlusion and low SNR conditions, while enabling efficient real-time infrared small target detection.…”
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CSF-YOLO: A Lightweight Model for Detecting Grape Leafhopper Damage Levels by Chaoxue Wang, Leyu Wang, Gang Ma, Liang Zhu

“…The model employs FasterNet as the backbone network to enhance computational efficiency and reduce model complexity. It substitutes for the nearest-neighbor upsampling with CARAFE to improve small target detection capabilities. …”
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Detection of weeds in vegetables using image classification neural networks and image processing by Huiping Jin, Kang Han, Kang Han, Hongting Xia, Bo Xu, Xiaojun Jin

“…However, the wide variety of weed types and their complex distribution creates difficulties in rapid and accurate weed detection. …”
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Study of conveyor belt deviation detection based on improved YOLOv8 algorithm by Yunfeng Ni, Haixin Cheng, Ying Hou, Ping Guo

“…Abstract Conveyor belt deviation is a commmon and severe type of fault in belt conveyor systems, often resulting in significant economic losses and potential environment pollution. Traditional detection methods have obvious limitations in fault localization precision and analysis accuracy, unable to meet the demands of efficient and real-time fault detection in complex industrial scenarios. …”
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CIDNet: A Maritime Ship Detection Model Based on ISAR Remote Sensing by Fei Liu, Boyang Liu, Hang Zhou, Song Han, Kunlin Zou, Wenjie Lv, Chang Liu

“…The model is based on the Boundary Box Efficient Transformer (BETR) architecture, which combines super-resolution preprocessing, a deep feature extraction network, a feature fusion technique, and a coordinate maintenance mechanism to improve the detection accuracy and real-time performance of ship targets in complex settings. …”
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Ship detection optimization method in SAR imagery based on multi-feature weighting by Quanhua ZHAO, Xiao WANG, Yu LI, Guanghui WANG

“…Aiming at the problem that the accuracy of traditional ship detection algorithms is not satisfying in complex scene with many false alarm targets,a ship detection optimization method in SAR imagery based on multi-feature weighting was proposed.Firstly,the marker-based watershed algorithm was employed to remove land from SAR amplitude image.Then,the CFAR algorithm based on log-normal distribution was used to obtain candidate targets from no land image.Furthermore,the length to width ratio,the ship area and the contrast ratio of the candidate targets were extracted.Finally,a variance coefficient method was proposed to distribute the weight of the three features,and the confidence levels were calculated by combining the normalized feature vectors of the candidate targets with the feature weight.By determining the best confidence level,false alarm targets among the candidate targets were removed to optimize ship detection results.In order to verify the proposed method,experiments were carried on with the GF-3 SAR images of different complex scenes.The experimental results show that the proposed method is feasible and effective.…”
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An image processing technique for optimizing industrial defect detection using dehazing algorithms. by Xuanyi Zhao, Xiaohan Dou, Gengpei Zhang

“…In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …”
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Enhanced anomaly traffic detection framework using BiGAN and contrastive learning by Haoran Yu, Wenchuan Yang, Baojiang Cui, Runqi Sui, Xuedong Wu

“…However, existing methods face many challenges when processing complex high-dimensional traffic data. Especially in dealing with redundant features, data sparsity and nonlinear features, traditional methods often suffer from high computational complexity and low detection efficiency. …”
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Increase the effectiveness of radar detection systems by adapting to the spatial orientation of the object of observation by S. N. Yarmolik, M. V. Svinarskiy, A. S. Khramenkov, E. V. Zaiko

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Performance Comparison of Random Forest and Decision Tree Algorithms for Anomaly Detection in Networks by Rafiq Fajar Ramadhan, Wahid Miftahul Ashari

“…Despite the small difference in accuracy, Decision Tree demonstrated faster prediction times, making it more efficient for time-sensitive applications. This research concludes that while Random Forest provides higher accuracy for complex datasets, Decision Tree offers a more time-efficient solution with comparable accuracy.…”
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IMPLEMENTATION OF K-MEDOIDS AND K-PROTOTYPES CLUSTERING FOR EARLY DETECTION OF HYPERTENSION DISEASE by Hardianti Hafid, Selvi Annisa

“…The clustering results show K-Medoids' superiority in grouping data with higher Silhouette Coefficient values ​​compared to K-Prototypes. Overall, the K-Medoids and K-Prototypes algorithms can detect early hypertension risk by dividing patients into different risk groups. …”
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Mode Selection Model for Rail Crack Detection Based on Ultrasonic Guided Waves by Bo Xing, Zujun Yu, Xining Xu, Liqiang Zhu, Hongmei Shi

“…By setting a reasonable vibration coefficient and orthogonal coefficient, the mode with the highest sensitivity to cracks is selected for crack detection. …”
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Classifying IoT Botnet Attacks With Kolmogorov-Arnold Networks: A Comparative Analysis of Architectural Variations by Phuc Hao do, Tran Duc Le, Truong Duy Dinh, van Dai Pham

“…Variations such as Fast-KAN and Deep-KAN offered favorable trade-offs between computational efficiency and modeling capacity, making them suitable for real-time and resource-constrained IoT environments. …”
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Complex PM2.5 Pollution and Hospital Admission for Respiratory Diseases over Big Data in Cloud Environment by Yi Zhou, Lianshui Li

“…Cloud computing may be an efficient and low-cost way to solve this problem. This paper investigates a problem of a complex system: the impact of PM2.5 on hospitalization for respiratory diseases. …”
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FSDN-DETR: Enhancing Fuzzy Systems Adapter with DeNoising Anchor Boxes for Transfer Learning in Small Object Detection by Zhijie Li, Jiahui Zhang, Yingjie Zhang, Dawei Yan, Xing Zhang, Marcin Woźniak, Wei Dong

“…This approach achieves a balance between computational efficiency for medium-resolution images and the accuracy required for small object detection. …”
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YOLO-SRSA: An Improved YOLOv7 Network for the Abnormal Detection of Power Equipment by Wan Zou, Yiping Jiang, Wenlong Liao, Songhai Fan, Yueping Yang, Jin Hou, Hao Tang

“…Existing models have high false and missed detection rates in complex weather and multi-scale equipment scenarios. …”
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Graph-contrast ransomware detection (GCRD) with advanced feature selection and deep learning by Suneeta Satpathy, Pratik Kumar Swain

“…The present study proposes an efficient and scalable early-stage ransomware detection solution with further potential for improvement through dynamic runtime behaviour analysis of future cyber threats.…”
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