Small-Target Detection Algorithm Based on STDA-YOLOv8 by Cun Li, Shuhai Jiang, Xunan Cao

“…The CAM introduces multi-scale dilated convolutions, where convolutional kernels with different dilation rates capture contextual information from various receptive fields, enabling more accurate extraction of small-target features. …”
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Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy Conditions by Mauro Larrat, Claudomiro Sales

“…This study evaluates different machine learning algorithms in detecting and identifying drones using radar data from a 60 GHz millimeter-wave sensor. …”
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Study on Quality Assessment Methods for Enhanced Resolution Graph-Based Reconstructed Images in 3D Capacitance Tomography by Robert Banasiak, Mateusz Bujnowicz, Anna Fabijańska

“…However, given the recent advancements in Graph Convolutional Neural Networks (GCNs) for improving ECT image reconstruction, reliable Quality Assessment methods are essential for comparing the performance of different GCN models. …”
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Optimal Feature Selection and Classification for Parkinson’s Disease Using Deep Learning and Dynamic Bag of Features Optimization by Aarti, Swathi Gowroju, Mst Ismat Ara Begum, A. S. M. Sanwar Hosen

“…The framework’s adaptability to different datasets further highlights its versatility and potential for further medical applications. …”
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A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone by Amine Lagzouli, Peter Pivonka, David M. L. Cooper, Vittorio Sansalone, Alice Othmani

“…We trained DBAHNet on a limited dataset of 3D µCT scans of mouse tibiae and evaluated its performance on a diverse dataset collected from seven different research studies. This evaluation covered variations in resolutions, ages, mouse strains, drug treatments, surgical procedures, and mechanical loading. …”
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Studying Forgetting in Faster R-CNN for Online Object Detection: Analysis Scenarios, Localization in the Architecture, and Mitigation by Baptiste Wagner, Denis Pellerin, Sylvain Huet

“…We analyse the effectiveness of different types of recall in mitigating forgetting and show that CR outperforms existing methods.…”
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Semantic Segmentation of Brain Tumors Using a Local–Global Attention Model by Shuli Xing, Zhenwei Lai, Junxiong Zhu, Wenwu He, Guojun Mao

“…Additionally, the morphology and size of tumors can vary significantly among different patients. These factors pose considerable challenges for the precise segmentation of tumors and subsequent diagnosis. …”
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Drainage Pipeline Multi-Defect Segmentation Assisted by Multiple Attention for Sonar Images by Qilin Jin, Qingbang Han, Jianhua Qian, Liujia Sun, Kao Ge, Jiayu Xia

“…The multiple attention method proposed in this paper was adopted for detection, instance segmentation, and pose detection in different public datasets, especially in the object detection of the coco128-seg dataset under the same condition. …”
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Lithium-Ion Battery State of Health Degradation Prediction Using Deep Learning Approaches by Talal Alharbi, Muhammad Umair, Abdulelah Alharbi

“…Obtained results shows that the highest testing RMSE (0.666) and MAPE (0.980) are observed during decentralized learning, while the centralized approach shows varying performance across different batteries. The decentralized approach effectively balances performance and privacy, highlighting the reliability of federated learning in SoH prediction for lithium-ion batteries.…”
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Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals by Xingguo Zhang, Tengfei Li, Maoxun Sun, Lei Zhang, Cheng Zhang, Yue Zhang

“…This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. …”
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Analysis of the Influence of Image Resolution in Traffic Lane Detection Using the CARLA Simulation Environment by Aron Csato, Florin Mariasiu, Gergely Csiki

“…The aim of this study is to show the influence of image resolution in traffic lane detection using a virtual dataset from virtual simulation environment (CARLA) combined with a real dataset (TuSimple), considering four performance parameters: Mean Intersection over Union (mIoU), F1 precision score, Inference time, and processed frames per second (FPS). By using a convolutional neural network (U-Net) specifically designed for image segmentation tasks, the impact of different input image resolutions (512 × 256, 640 × 320, and 1024 × 512) on the efficiency of traffic line detection and on computational efficiency was analyzed and presented. …”
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How to detect occluded crosswalks in overview images? Comparing three methods in a heavily occluded area by Yuanyuan Zhang, Joseph Luttrell, IV, Chaoyang Zhang

“…To address this challenge, this study explores different deep learning-based solutions, including the aerial-view method (AVM) and street-view method (SVM), which are commonly used, and a combination of them, i.e., the dual-perspective method (DPM). …”
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Quantum Vision Theory in Deep Learning for Object Recognition by Cem Direkoglu, Melike Sah

“…Quantum-scale world looks different from our human-scale world. Attempts to relate the microscopic quantum world to our macroscopic world led to philosophical issues and questions. …”
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A VMD-TCN-Based Method for Predicting the Vibrational State of Scaffolding in Super High-Rise Building Construction by Ping Zhu, Gen Liu, Jian Wang, Pengfei Wang

“…Additionally, the VMD-TCN model maintains high predictive accuracy across different sensor placements and data collection periods, demonstrating strong generalization capabilities. …”
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Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images by Zilong Lian, Yulin Zhan, Wenhao Zhang, Zhangjie Wang, Wenbo Liu, Xuhan Huang

“…Consequently, spatiotemporal fusion techniques, which integrate images from different sensors, have garnered significant attention. …”
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Automatic detection of developmental stages of molar teeth with deep learning by Ertuğrul Furkan Savaştaer, Berrin Çelik, Mahmut Emin Çelik

“…The stages of development of molar teeth were divided into 4 classes such as M1, M2, M3 and M4. 9 different convolutional neural network models, which were Cascade R-CNN, YOLOv3, Hybrid Task Cascade(HTC), DetectorRS, SSD, EfficientNet, NAS-FPN, Deformable DETR and Probabilistic Anchor Assignment(PAA), were used for automatic detection of these classes. …”
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A 3-dimension urban growth analysis using local climate zone mapping by Haojie Chen, Cheolhee Yoo, Jacqueline T. Y. Lo

“…This study proposes an approach to characterize a city’s volumetric expansion for exploring urban land changes based on Local Climate Zone (LCZ) mapping by using a new convolutional neural network (CNN) model termed as DenseNetLCZ. …”
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Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning by Zihan Wang, Jinjin Wu, Chenbei Li, Bing Wang, Qingxia Wu, Lan Li, Huijie Wang, Chao Tu, Jianhua Yin

“…The accuracy and true positivity of the multimodal diagnostic model were significantly improved to 0.8495 and 0.9412, respectively, compared to those of the single-modal models. Besides, the difference of tissue microenvironments before and after cancerization can be used as a basis for cancer diagnosis, and the information extraction and intelligent diagnosis of osteosarcoma tissue can be achieved by using multimodal microscopic imaging technology combined with deep learning, which significantly promoted the application of tissue microenvironment in pathological examination. …”
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Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data by Haytam Elyoussfi, Abdelghani Boudhar, Salwa Belaqziz, Mostafa Bousbaa, Karima Nifa, Bouchra Bargam, Abdelghani Chehbouni

“…Study focus: The research integrates remote sensing data, particularly the Normalized-Difference Snow Index (NDSI) from the MODIS Sensor, with machine learning (ML) and deep learning (DL) models to predict daily snow depth (DSD) at a local scale. …”
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Dual Attention-Based Global-Local Feature Extraction Network for Unsupervised Change Detection in PolSAR Images by Dazhi Xu, Ming Li, Yan Wu, Peng Zhang, Xinyue Xin

“…First, we use fuzzy C-means clustering on the enhanced Shannon entropy difference image to automatically generate pseudolabeled samples required for unsupervised CD. …”
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