PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs by Raokaijuan Wang, Fangyuan Cheng, Guangsheng Dai, Jiayu Zhang, Chengmin Fan, Jinghong Yu, Juan Li, Fulin Jiang

“…Based on the promotion in segmentation, the accuracy of abnormal tooth morphology detection exceeded 0.957 and was 4.3% higher. A website was constructed to assist in PX interpretation, and the diagnostic efficiency was greatly enhanced with the assistance of PXseg. …”
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Infrared Small Target Detection Based on Compound Eye Structural Feature Weighting and Regularized Tensor by Linhan Li, Xiaoyu Wang, Shijing Hao, Yang Yu, Sili Gao, Juan Yue

“…Furthermore, to improve detection speed, the structural tensor of the effective imaging region in infrared compound eye images is reconstructed, and the Representative Coefficient Total Variation method is employed to avoid complex singular value decomposition and regularization optimization computations. …”
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A Low-Cost Antipodal Vivaldi Antenna-Based Peanut Defect Rate Detection System by Yuanyuan Yin, Fangyan Ma, Xiaohong Liu, Shuhao Wang, Junjie Xia, Liqing Zhao

“…Differing from traditional and often expensive or complex detection methods, our approach utilizes a low-cost antipodal Vivaldi antenna, complemented by a custom-designed defect rate detection system. …”
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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

“…Abstract Accurate and efficient insulator defect detection is critical for power grid reliability, but it can be affected by the presence of noises in captured images and can be difficult to employ for real‐time operation due to the slow processing of the detection scheme. …”
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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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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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MonoAMP: Adaptive Multi-Order Perceptual Aggregation for Monocular 3D Vehicle Detection by Xiaoxi Hu, Tao Chen, Wentao Zhang, Guangyi Ji, Hongxia Jia

“…Monocular 3D object detection is rapidly emerging as a key research direction in autonomous driving, owing to its resource efficiency and ease of implementation. …”
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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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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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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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Real-Time Vehicle Detection Using Cross-Correlation and 2D-DWT for Feature Extraction by Abdelmoghit Zaarane, Ibtissam Slimani, Abdellatif Hamdoun, Issam Atouf

“…Nowadays, real-time vehicle detection is one of the biggest challenges in driver-assistance systems due to the complex environment and the diverse types of vehicles. …”
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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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FEVT-SAR: Multicategory Oriented SAR Ship Detection Based on Feature Enhancement Vision Transformer by Minding Fang, Yu Gu, Dongliang Peng

“…Issues such as complex noise interference and the long-tail distribution of data present many challenges to the multicategory ship detection task in synthetic aperture radar (SAR) images. …”
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A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023 by Shalini Kumari, Chander Prabha, Asif Karim, Md. Mehedi Hassan, Sami Azam

“…Further, research is needed to create robust, efficient techniques that generalize datasets and detect anomalies in complex industrial images. …”
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PNet-IDS: A Lightweight and Generalizable Convolutional Neural Network for Intrusion Detection in Internet of Things by Auwal Sani Iliyasu, Abdul Jabbar Siddiqui, Houbing Song, Fahad Jibrin Abdu

“…By combining PNet-IDS’ efficiency with knowledge distillation’s adaptability, the proposed method offers a scalable and resilient solution for IoT intrusion detection.…”
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K-Gen PhishGuard: an Ensemble Approach for Phishing Detection with K-Means and Genetic Algorithm by Ali Al-Hafiz, Adnan Jabir, Shamala Subramaniam

“… Phishing detection is considered a critical problem in cybersecurity, and utilising machine learning with an efficient feature selection method for precisely identifying malicious websites is deemed the most critical challenge. …”
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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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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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