Adapting Vision Transformer-Based Object Detection Model for Handwritten Text Line Segmentation Task by Osman Furkan KARAKUŞ, Ayla GULCU, Ali Can KARACA

“…We benefit from DETR model with a ResNet-101 backbone pretrained on the Common Objects in Context (COCO) object detection training dataset, and re-train this model using our novel, complex line segmentation dataset consisting of 1,610 handwritten forms. …”
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Improved Cylinder-Based Tree Trunk Detection in LiDAR Point Clouds for Forestry Applications by Shaobo Ma, Yongkang Chen, Zhefan Li, Junlin Chen, Xiaolan Zhong

“…Together, the other two algorithms failed to detect. The findings highlight the RANSAC-CyF algorithm’s superior accuracy, robustness, and adaptability in complex forest environments, significantly improving the efficiency and precision of individual tree trunk detection for forestry surveys and ecological research.…”
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YOLOv8n-SSDW: A Lightweight and Accurate Model for Barnyard Grass Detection in Fields by Yan Sun, Hanrui Guo, Xiaoan Chen, Mengqi Li, Bing Fang, Yingli Cao

“…Moreover, the morphological similarity, overlapping, and occlusion between barnyard grass and rice pose challenges for reliable detection in complex environments. To address these issues, this study constructed a barnyard grass detection dataset using high-resolution images captured by a drone equipped with a high-definition camera in rice experimental fields in Haicheng City, Liaoning Province. …”
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An interpretable and adaptable data-driven model for performance prediction in thermal plants by G. Prokhorskii, M. Preißinger, S. Rudra, E. Eder

“…Data-driven models are a useful aid for monitoring and control of thermal power plants, but they require an effective feature selection to allow for an accurate, computationally efficient, and interpretable model. …”
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Low-cost video-based air quality estimation system using structured deep learning with selective state space modeling by Maqsood Ahmed, Xiang Zhang, Yonglin Shen, Tanveer Ahmed, Shahid Ali, Ayaz Ali, Aminjon Gulakhmadov, Won-Ho Nam, Nengcheng Chen

“…This study proposes Air Quality Prediction-Mamba (AQP-Mamba), a video-based deep learning model that integrates a structured Selective State Space Model (SSM) with a selective scan mechanism and a hybrid predictor (HP) to estimate air quality. …”
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Dynamic Reporting Nodes Selection Method for Network Awareness Based on Active–Passive Integrated Network Telemetry in LEO Satellite Networks by Hang Di, Tao Dong, Zhihui Liu, Shuotong Wei, Qiwei Zhang, Dingyun Zhang

“…Satellite network awareness is a key technology for obtaining the status of onboard resources and supporting efficient network management. However, the dynamic characteristics of the network topology results in dynamic transmission delays of awareness data, while the instability of satellite-ground links poses challenges in selecting awareness reporting nodes. …”
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Synergistic Multi-Model Approach for GPR Data Interpretation: Forward Modeling and Robust Object Detection by Hang Zhang, Zhijie Ma, Xinyu Fan, Feifei Hou

“…The proposed method achieves a mean average precision (mAP) of 0.579 on synthetic datasets, outperforming YOLOv3 and RetinaNet by 16.0% and 23.5%, respectively, while maintaining robust multi-object detection in complex field conditions.…”
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Attention-Based LiDAR–Camera Fusion for 3D Object Detection in Autonomous Driving by Zhibo Wang, Xiaoci Huang, Zhihao Hu

“…To address the challenges of cross-modal feature interaction in LiDAR–image fusion and the low recognition efficiency/positioning accuracy of traffic participants in dense traffic flows, this study proposes an attention-based 3D object detection network integrating point cloud and image features. …”
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IR-ADMDet: An Anisotropic Dynamic-Aware Multi-Scale Network for Infrared Small Target Detection by Ning Li, Daozhi Wei

“…Infrared small target detection in complex environments remains a significant challenge due to low signal-to-noise ratios (SNRs), background clutter, and target scale variations. …”
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Oak-YOLO: A high-performance detection model for automated Oak seed defect identification. by Hao Li, Zhuqi Li, Dongkui Chen, Wangyu Wu, Xuanlong He, Hongbo Mu

“…To address this challenge, we developed a high-resolution imaging system and proposed an improved YOLO-based model named Oak-YOLO for efficient and accurate defect detection in oak seeds. …”
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Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review by Boris I, Kseniia Barashok, Yongjoon Choi, Yeongil Choi, Mohammed Aslam, Jaesun Lee

“…However, conventional ultrasonic approaches face challenges in analyzing complex signals, limiting their accuracy and efficiency in certain applications. …”
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DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETR by Xiaolong Wei, Ling Yin, Liangliang Zhang, Fei Wu

“…These improvements not only enhance the model’s accuracy and efficiency but also provide practical significance in complex, high-density urban environments, supporting real-world applications in UAV-based surveillance and monitoring tasks.…”
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Deep Learning-Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks by Yange Li, Han Wei, Zheng Han, Jianling Huang, Weidong Wang

“…Video monitoring systems provide a large amount of unstructured image data on-site for this purpose, however, requiring a computer vision-based automatic solution for real-time detection. Although a growing body of literature has developed many deep learning-based models to detect helmet for the traffic surveillance aspect, an appropriate solution for the industry application is less discussed in view of the complex scene on the construction site. …”
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Detection of the Pin Defects of Power Transmission Lines Based on Improved TPH-MobileNetv3 by Mengxuan Li, Jingshan Han, Zhi Yang, Bin Zhao, Peng Liu

“…In response to the large size, complex background, and on-site requirements, such as real-time detection, of power transmission lines, this paper proposes a method to detect pin defects based on TPH-MobileNetv3 (Transformer prediction Head Mobilenetv3). …”
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Study on Spark Image Detection for Abrasive Belt Grinding via Transfer Learning with YOLOv8 by Jian Huang, Guangpeng Zhang

“…Aiming to solve the problems of low precision and poor efficiency caused by relying on manual experience during the manual polishing of blades, a multi-view spark image detection method based on YOLOv8 transfer learning is proposed. …”
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A target detection model HR-YOLO for advanced driver assistance systems in foggy conditions by Yao Zhang, Na Jia

“…To enhance detection performance under complex meteorological conditions, several critical modules have been optimized. …”
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DSFA-SwinNet: A Multi-Scale Attention Fusion Network for Photovoltaic Areas Detection by Shaofu Lin, Yang Yang, Xiliang Liu, Li Tian

“…However, due to the variability in scale and shape of PV installations in complex environments, the detection results often fail to capture detailed information and struggle to scale for multi-scale PV systems. …”
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RRFNet: A free-anchor brain tumor detection and classification network based on reparameterization technology. by Wei Liu, Xingxin Guo

“…Conventional object detection models for brain tumor detection face limitations in brain tumor detection owing to the significant differences between medical images and natural scene images, as well as challenges such as complex backgrounds, noise interference, and blurred boundaries between cancerous and normal tissues. …”
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A Vision-Based Method for Detecting the Position of Stacked Goods in Automated Storage and Retrieval Systems by Chuanjun Chen, Junjie Liu, Haonan Yin, Biqing Huang

“…While computer vision and deep learning offer promising solutions, existing methods struggle to balance detection accuracy, computational efficiency, and environmental adaptability. …”
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AC-YOLO: A lightweight ship detection model for SAR images based on YOLO11. by Rui He, Dezhi Han, Xiang Shen, Bing Han, Zhongdai Wu, Xiaohu Huang

“…However, existing SAR ship detection algorithms encounter two major challenges: limited detection accuracy and high computational cost, primarily due to the wide range of target scales, indistinct contour features, and complex background interference. …”
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