Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks by Mirosław Łącki

“…The article describes the use of deep neural networks to detect small floating objects located in a vessel’s path. …”
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Identification method for wheel/rail tread defects based on integrated partial convolutional network by CHENG Xiang, HE Jing, ZHANG Changfan, JIA Lin

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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

“…The advancement of Transformer models in computer vision has rapidly spurred numerous Transformer-based object detection approaches, such as DEtection TRansformer. …”
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Detecting Activities of Daily Living in Egocentric Video to Contextualize Hand Use at Home in Outpatient Neurorehabilitation Settings by Adesh Kadambi, Jose Zariffa

“…We evaluated our models on a complex dataset collected in the wild comprising 2261 minutes of egocentric video from 16 participants with impaired hand function. By leveraging pre-trained object detection and hand-object interaction models, our system achieves robust performance across different impairment levels and environments, with our best model achieving a mean weighted F1-score of <inline-formula> <tex-math notation="LaTeX">$0.78~\pm ~0.12$ </tex-math></inline-formula> and maintaining an F1-score over 0.5 for all participants using leave-one-subject-out cross validation. …”
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Non-Line-of-Sight Location With Gauss Filtering Algorithm Based on a Model of Photon Flight by Yu Ren, Zongliang Xie, Yihan Luo, Shaoxiong Xu, Haotong Ma, Yi Tan

“…In order to simulate the NLOS location of a hidden object, we derive the signal scattered by the object and build a model of photon flight based on photon scattering and propagation. …”
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High Detection Rate of Posterolateral Tibial Plateau Fractures and Poor Functional Outcomes in Type IIIB Impaction Fractures After Anterior Cruciate Ligament Rupture and Reconstruc... by Shijie Jiang, Weizhi Ren, Ruixia Zhu, Dimitris Dimitriou, Rongshan Cheng, Xiaojun Jia, Dong Zheng, Yuji Wang, Wei Xu

“…The purpose of the present study was to report the detection rate of the posterolateral tibial plateau impaction fractures in patients with ACL ruptures, and to evaluate the functional outcomes of patients following ACL reconstruction (ACLR) without treatment of the tibial fractures at a 2‐year postoperative follow‐up. …”
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SSOD-QCTR: Semi-Supervised Query Consistent Transformer for Optical Remote Sensing Image Object Detection by Xinyu Ma, Pengyuan Lv, Xunqiang Gong

“…This paper proposes a semi-supervised query consistent transformer for optical remote sensing image object detection (SSOD-QCTR). A detection transformer (DETR)-like model is adopted as the basic network, and it follows the teacher–student training scheme. …”
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A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology by Mustafa N. Abbas, David Broneske, Gunter Saake

“…This paper presents two primary contributions: First, it proposes a novel multi-objective optimization model for detecting protein complexes, conceptualizing the task as a problem with inherently conflicting objectives based on biological data. …”
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Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural Network by Yang Liu, Mengfei Gao, Humin Zong, Xinping Wang, Jinshuang Li

“…The nonmaximum suppression and loss function improvement methods of the MYOLO-lite network model are proposed for occluded tracks in front of the train, obstruction of trains, low detection accuracy of large object coincidence, and uneven distribution of positive and negative samples. …”
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TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors by Adonisz Dimitriu, Tamás Vilmos Michaletzky, Viktor Remeli

“…Furthermore, these adversarial patterns exhibit strong transferability to other object detection models such as Faster R-CNN and earlier YOLO versions.…”
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Towards Efficient Object Detection in Large-Scale UAV Aerial Imagery via Multi-Task Classification by Shuo Zhuang, Yongxing Hou, Di Wang

“…Achieving rapid and effective object detection in large-scale unmanned aerial vehicle (UAV) images presents a challenge. …”
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Lightweight coal mine conveyor belt foreign object detection based on improved Yolov8n by Jierui Ling, Zhibo Fu, Xinpeng Yuan

“…Lastly, the original CIoU loss function in Yolov8n is replaced with MPDIoU, which allows the model to more accurately predict the position and shape of bounding boxes in the object detection task, thereby further reducing accuracy loss. …”
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Toward Efficient UAV-Based Small Object Detection: A Lightweight Network with Enhanced Feature Fusion by Xingyu Di, Kangning Cui, Rui-Feng Wang

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Redefining Object Detection for Open-World Settings: A Framework for Simultaneous Identification of Known and Unknown Classes by Muhammad Ali Iqbal, Yeo Chan Yoon, Soo Kyun Kim

“…To effectively tackle these challenges, this study presents a new Open-World Novelty and Outlier Detection (OWNOD). The framework integrates a dynamic thresholding mechanism a customized loss function and statistical verification technique optimize the simultaneous detection of both known and unknown objects. …”
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DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles by Siqiang Cheng, Lingshan Chen, Kun Yang

“…The key innovation lies in the synergistic integration of several architectural enhancements: the DCNv3_LKA_C2f module, leveraging Deformable Convolution v3 (DCNv3) and Large Kernel Attention (LKA) for better the capture of complex object shapes; an Optimized Feature Pyramid Network structure (Optimized-GFPN) for improved multi-scale feature fusion; the Detect_SA module, incorporating spatial Self-Attention (SA) at the detection head for broader context awareness; and an Inner-Shape Intersection over Union (IoU) loss function to improve bounding box regression accuracy. …”
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MXT-YOLOv7t: An Efficient Real-Time Object Detection for Autonomous Driving in Mixed Traffic Environments by Afdhal Afdhal, Khairun Saddami, Mirshal Arief, Sugiarto Sugiarto, Zahrul Fuadi, Nasaruddin Nasaruddin

“…A critical part of this system is object detection, which enables the vehicle to perceive and interpret its surroundings. …”
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Deep learning-based image classification of sea turtles using object detection and instance segmentation models. by Jong-Won Baek, Jung-Il Kim, Chang-Bae Kim

“…Recently, instance segmentation models have been developed to address this issue by providing more accurate classification of complex images compared to traditional object detection models. This study compared the performance of two state-of-the-art DL methods namely; the object detection model (YOLOv5) and instance segmentation model (YOLOv5-seg), to detect and classify sea turtles. …”
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MMPW-Net: Detection of Tiny Objects in Aerial Imagery Using Mixed Minimum Point-Wasserstein Distance by Nan Su, Zilong Zhao, Yiming Yan, Jinpeng Wang, Wanxuan Lu, Hongbo Cui, Yunfei Qu, Shou Feng, Chunhui Zhao

“…The detection of distant tiny objects in aerial imagery plays a pivotal role in early warning, localization, and recognition tasks. …”
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A Multi-Objective Sensor Placement Method Considering Modal Identification Uncertainty and Damage Detection Sensitivity by Xue-Yang Pei, Yuan Hou, Hai-Bin Huang, Jun-Xing Zheng

“…The method introduces two key objective functions: minimizing modal identification uncertainty by leveraging Bayesian modal identification theory and information entropy and maximizing damage detection sensitivity by incorporating an entropy-based measure to quantify the uncertainty in stiffness variation estimation. …”
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Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers by Ruslan Abdulkadirov, Pavel Lyakhov, Denis Butusov, Nikolay Nagornov, Dmitry Reznikov, Anatoly Bobrov, Diana Kalita

“…In this area, deep neural networks are used to solve routine object detection problems, satisfying the required rules and conditions. …”
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