Improving small object detection via cross-layer attention by Ru Peng, Guoran Tan, Xingyu Chen, Xuguang Lan

“…Experiments show that our approach achieves consistent improvements in both object detection and instance segmentation, which demonstrates the effectiveness of our approach.…”
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Unsupervised selective labeling for semi-supervised industrial defect detection by Jian Ge, Qin Qin, Shaojing Song, Jinhua Jiang, Zhiwei Shen

“…In industrial detection scenarios, achieving high accuracy typically relies on extensive labeled datasets, which are costly and time-consuming. …”
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RADAR: Reasoning AI-Generated Image Detection for Semantic Fakes by Haochen Wang, Xuhui Liu, Ziqian Lu, Cilin Yan, Xiaolong Jiang, Runqi Wang, Efstratios Gavves

“…Specifically, RADAR contains a specialized multimodal LLM to process given images and detect semantic fakes. To improve the generalization ability, we further incorporate ChatGPT as an assistor to detect unrealistic components in grounded text descriptions. …”
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Tampered text detection via RGB and frequency relationship modeling by Yuxin WANG, Boqiang ZHANG, Hongtao XIE, Yongdong ZHANG

“…In recent years, the widespread dissemination of tampered text images on the Internet constitutes an important threat to the security of text images.However, the corresponding tampered text detection (TTD) methods have not been sufficiently explored.The TTD task aims to locate all text regions in an image while judging whether the text regions have been tampered with according to the authenticity of the texture.Thus, different from the general text detection task, TTD task further needs to perceive the fine-grained information for real-world and tampered text classification.TTD task has two main challenges.One the one hand, due to the high similarity in texture between real-world texts and tampered texts, TTD methods that only learn from RGB domain features have limited capability to distinguish these two-category texts well.On the other hand, as the different detecting difficulty exists in real-world texts and tampered texts, the network cannot well balance the learning process of the two-category texts, resulting in the imbalance detection performance between real-world and tampered texts.Compared with RGB domain features, the discontinuity of text texture in frequency domain can help the network to identify the authenticity of text instances.Accordingly, a new TTD method based on RGB and frequency information relationship modeling was proposed.The features in the RGB and frequency domains were extracted by independent feature extractors respectively.Thus, the identification ability of tampered texture can be enhanced by introducing frequency information during the texture perception.Then, a global RGB-frequency relationship module (GRM) was introduced to model the texture authenticity relationship between different text instances.GRM referred to the RGB-frequency features of other text instances in the same image to assist in judging the authenticity of the current text instance, which solved the problem of imbalanced detection performance.Furthermore, a new TTD dataset (Tampered-SROIE) was proposed to evaluate the effectiveness of proposed method, which contains 986 images (626 training images and 360 test images).By evaluating on the Tampered-SROIE, the proposed method obtains 95.97% and 96.80% in F-measure for real-world and tampered texts respectively and reduces the imbalanced detection accuracy by 1.13%.The proposed method will give new insights to the TTD community from the perspective of network structure and detection strategy.Tampered-SROIE also provides an evaluation benchmark for future TTD methods.…”
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Satellite-Based Forest Stand Detection Using Artificial Intelligence by Patrik Kovacovic, Rastislav Pirnik, Julia Kafkova, Mario Michalik, Alzbeta Kanalikova, Pavol Kuchar

“…An optimal model was selected based on parameters such as detection accuracy, total training time, and the precision of labeling detected image elements. …”
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Zero‐shot insect detection via weak language supervision by Benjamin Feuer, Ameya Joshi, Minsu Cho, Shivani Chiranjeevi, Zi Kang Deng, Aditya Balu, Asheesh K. Singh, Soumik Sarkar, Nirav Merchant, Arti Singh, Baskar Ganapathysubramanian, Chinmay Hegde

“…These large datasets (for instance, citizen science data curation platforms like iNaturalist) can pave the way for developing powerful artificial intelligence (AI) models for detection and counting. …”
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Multi-Modal Analysis of Bi-Parametric MRI Slices for Lesion Detection in Prostate Cancer Screening by Davide Antonutti, Axel de Nardin, Silvia Zottin, Claudio Piciarelli, Gian Luca Foresti

“…Prostate cancer is a leading cause of cancer-related mortality among men, with early detection playing a critical role in improving patient outcomes. …”
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An extrinsic random-based ensemble approach for android malware detection by Nektaria Potha, V. Kouliaridis, G. Kambourakis

“…Malware detection is a fundamental task and associated with significant applications in humanities, cybersecurity, and social media analytics. …”
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Improved YOLOv5: Efficient Object Detection for Fire Images by Dongxing Yu, Shuchao Li, Zhongze Zhang, Xin Liu, Wei Ding, Xinyi Zhao

“…After training and testing in fire detection instances, the default versions of the YOLO technique exhibit a significantly low level of accuracy. …”
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Automated Detection of Deviations in Bankruptcy Processes Using Process Mining by Ivan P. Malashin, Igor S. Masich, Daniel A. Ageev, Dmitriy A. Evsyukov, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin, Vadim S. Tynchenko

“…The framework is evaluated using real-world bankruptcy case data, demonstrating its capability to detect and analyze process inefficiencies.…”
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Fatigue Detection of Air Traffic Controllers Through Their Eye Movements by Yi Hu, Haoran Shen, Hui Pan, Wenbin Wei

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Automatic large-scale political bias detection of news outlets. by Ronja Rönnback, Chris Emmery, Henry Brighton

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Crack Detectability in Vertical Axis Cooling Pumps During Operation by N. Bachschmid, P. Pennacchi, E. Tanzi, P. Verrier, F. Hasnaoui, K. Aabadi

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Toward Causal and Evidential Open-Set Temporal Action Detection by Zhuoyao Wang, Rui-Wei Zhao, Rui Feng, Cheng Jin

“…Temporal action detection (TAD) is a critical task in video understanding. …”
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Machine learning-driven strategies for enhanced pediatric wheezing detection by Hye Jeong Moon, Hyunmin Ji, Hyunmin Ji, Baek Seung Kim, Beom Joon Kim, Kyunghoon Kim, Kyunghoon Kim

“…To overcome these limitations, artificial intelligence models have been developed.MethodsIn this prospective study, we aimed to compare respiratory sound feature extraction methods to develop an optimal machine learning model for detecting wheezing in children. Pediatric pulmonologists recorded and verified 103 instances of wheezing and 184 other respiratory sounds in 76 children. …”
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GenAI-Based Models for NGSO Satellites Interference Detection by Almoatssimbillah Saifaldawla, Flor Ortiz, Eva Lagunas, Abuzar B. M. Adam, Symeon Chatzinotas

“…Despite existing radio regulations during the filing stage, this heightened congestion in the spectrum is likely to lead to instances of interference during real-time operations. …”
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Enhancing Online Security: A Novel Machine Learning Framework for Robust Detection of Known and Unknown Malicious URLs by Shiyun Li, Omar Dib

“…The resulting malicious URL detection system (MUDS) combines supervised machine learning techniques, tree-based algorithms, and advanced data preprocessing, achieving a high detection accuracy of 96.83% for known MURLs. …”
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Detection of GNSS spoofed signals based on the weighted moving average bias correction method by Chuanyu Wu, Yuanfa Ji, Xiyan Sun

“…Abstract Spoofing intrusions pose a major threat to user security by delivering incorrect information. The detection rate of existing signal quality monitoring (SQM) metrics notably decreases when faced with numerous specific combinations of code phases and carrier phases in spoofing signal instances. …”
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An annotated high-content fluorescence microscopy dataset with EGFP-Galectin-3-stained cells and manually labelled outlineszenodo by Salma Kazemi Rashed, Malou Arvidsson, Rafsan Ahmed, Sonja Aits

“…Outlines were labelled by three annotators, who had high inter-annotator agreement between them and with a biomedical expert, who labelled some of the objects for comparison and reviewed a subset of the labels, making minor corrections as needed.The dataset comprises over 2200 annotated cell objects in total, making it sufficient in size to train high-performing neural networks for instance or semantic segmentation. Labels can also easily be converted to boxes for object detection tasks. …”
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Fuel detection in forest environments training deep learners with smartphone imagery by F. Pirotti, F. Pirotti, A. Carmelo, E. Kutchartt, E. Kutchartt, E. Kutchartt

“…The results indicate that the "Extra-large Instance Segmentation" model achieved the best performance with F1-score value of 0.79 at a confidence of 0.763 on familiar images in the validation phase with 214 epochs, whereas the "Large Instance Segmentation" model was more effective on new images in the test phase, as expected with a lower F1-score of 0.24 and a confidence value of 0.492. …”
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