MRC-DETR: A High-Precision Detection Model for Electrical Equipment Protection in Power Operations by Shenwang Li, Yuyang Zhou, Minjie Wang, Li Liu, Thomas Wu

“…This paper introduces MRC-DETR (Multi-Scale Re-calibration Detection Transformer), a novel framework for detecting Power Engineering Personal Protective Equipment (PEPPE) in complex electrical operating environments. …”
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An Improved Lithium-Ion Battery Fire and Smoke Detection Method Based on the YOLOv8 Algorithm by Li Deng, Di Kang, Quanyi Liu

“…This paper introduces a novel algorithm—YOLOv8 (You Only Look Once version 8) + FRMHead (a multi-branch feature refinement head) + Slimneck (a lightweight bottleneck module), abbreviated as YFSNet—for lithium-ion battery fire and smoke detection in complex backgrounds. By integrating advanced modules for richer feature extraction and streamlined architecture, YFSNet significantly enhances detection precision and real-time performance. …”
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MSUD-YOLO: A Novel Multiscale Small Object Detection Model for UAV Aerial Images by Xiaofeng Zhao, Hui Zhang, Wenwen Zhang, Junyi Ma, Chenxiao Li, Yao Ding, Zhili Zhang

“…Due to the objects in UAV aerial images often presenting characteristics of multiple scales, small objects, complex backgrounds, etc., the performance of object detection using current models is not satisfactory. …”
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Nanoscale investigations of complex microbial interactions with terrestrial and extraterrestrial minerals using STEM based approach: implications for life on Earth and beyond by Tetyana Milojevic, Mihaela Albu, Ilse Letofsky-Papst, Artem Mashchenko

“…During these investigations, we have developed an efficient combined approach of microbiology, wet chemistry and STEM based spectroscopy nanoanalysis. …”
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Multi-target detection and tracking based on CRF network and spatio-temporal attention for sports videos by Xi Chen, Hui Zhang, Achyut Shankar, Bharat Bhushan, Kireet Joshi

“…Therefore, the purpose of this article is to propose an efficient multi-target detection algorithm to quickly and effectively detect all target objects in the video. …”
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Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic Algorithm by Rui Liang, Dingzhao Li, Haixin Sun, Liangpo Hong

“…Efficient and adaptive formation planning is critical for unmanned surface vehicle (USV) swarms equipped with sensor networks and smart sensors to perform cooperative detection tasks in complex marine environments. …”
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Delamination Detection in CFRP Stringers with A₀-Mode Lamb Wave Curvature by Gaozheng Zhao, Yike Zeng, Zhenyang Yu, Kaihua Sun, Jian Chen

“…Ultrasonic detection of delamination in stringer-stiffened panels made of carbon fiber-reinforced plastics (CFRPs) presents a critical challenge due to their complex geometry, complicated properties and large size. …”
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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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Detection of Q235 Mild Steel Resistance Spot Welding Defects Based on EMD-SVM by Yuxin Wu, Xiangdong Gao, Dongfang Zhang, Perry Gao

“…Real-time detection of welding defects in resistance spot welding is a complex challenge. …”
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Detection of disease on nasal breath sound by new lightweight architecture: Using COVID-19 as an example by Jiayuan She, Lin Shi, Peiqi Li, Ziling Dong, Renxing Li, Shengkai Li, Liping Gu, Zhao Tong, Zhuochang Yang, Yajie Ji, Liang Feng, Jiangang Chen

“…Although many countries have reduced or stopped large-scale testing measures, the detection of such diseases remains a propriety. Objective This study aims to develop a novel, lightweight deep neural network for efficient, accurate, and cost-effective detection of COVID-19 using a nasal breathing audio data collected via smartphones. …”
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DFC-Net: Dual-Branch Collaborative Feature Enhancement for Cloud Detection in Remote Sensing Images by Wanting Zhou, Yan Mo, Qiaofeng Ou, Shaowei Bai

“…Although deep learning-based methods have achieved remarkable results, challenges remain in the fine-grained segmentation of cloud boundaries and the detection of thin clouds over complex land surfaces. …”
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Image processing‐based noise‐resilient insulator defect detection using YOLOv8x by Shagor Hasan, Md. Abdur Rahman, Md. Rashidul Islam, Animesh Sarkar Tusher

“…This paper proposes a novel framework based on the YOLOv8x object detection scheme, addressing the challenge of detecting small defects in complex aerial images and providing a noise mitigation scheme. …”
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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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Crack Defect Detection Method for Plunge Pool Corridor Based on the Improved U-Net Network by Chunyao Hou, Zhihui Liu, Fan Xia, Xun Zhou, Yuhang Shui, Yonglong Li

“…The improved U-Net model exhibits higher detection accuracy in corridor crack segmentation tasks, effectively extracting feature information from irregular shapes, and provides a superior solution for crack defect detection in a plunge pool corridor, thereby enhancing the safety and efficient maintenance of hydropower stations.…”
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Development and validation of mPCR-CEFA for detecting multiple deletion and non-deletion thalassemia genotypes by Jingping Xu, Jingping Xu, Baoyan Ren, Qixun Fang, Kangfeng Lin, Kangfeng Lin, Xingan Xing, Xingan Xing, Jingting Lin

“…The method demonstrated a high accuracy rate of 99.5%. It successfully detected complex compound genotypes like αCD 74α/−α4.2, βCD 17/βN and αWSα/--SEA, βCD 37/βN (or αWSα/--SEA, βCD 37/βCD 37), which were challenging for traditional approaches.ConclusionThe mPCR-CEFA method is a reliable, efficient, and scalable tool for genetic diagnosis of thalassemia. …”
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Deep learning-based automatic detection and grading of disk herniation in lumbar magnetic resonance images by Yan Guo, Xiaoxiang Huang, Wei Chen, Ichiro Nakamoto, Weiqing Zhuang, Hua Chen, Jie Feng, Jianfeng Wu

“…The greatest challenges today are the repetitive and time-consuming process of interpreting these complex MR images and the problem of unequal diagnostic results from physicians with different levels of experience. …”
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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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Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles by Naif Almakayeel

“…Machine learning (ML) techniques cannot detect every new and complex malware variant. The deep learning (DL) model is an efficient tool for detecting various malware variants. …”
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An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment by Julian R. Rice, G. Andrew Fricker, Jonathan Ventura

“…We compare this model to a number of high-performing baselines on a large and varied dataset in the Southern California region, and find that our method outperforms all baselines in terms of tree detection ability (75.5% F-score) and positional accuracy (2.28 meter root mean squared error), while being highly efficient. …”
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CommRad RF: A dataset of communication radio signals for detection, identification and classificationZenodo by Muhammad Usama Zahid, Muhammad Usman Akram, Muhammad Danish Nisar, Fahd Maqsood, Syed Usman Ali, Muhammad Montaha

“…This dataset can enhance the security of the communication channels by providing the possibility to analyse and detect any unauthorized source of transmission. Furthermore, we also propose two innovative deep learning models named Light Weight 1DCNN and Light Weight Bivariate 1DCNN, for efficient data processing and learning patterns from the complex dataset of radio signals.…”
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