Detection of conveyor belt defects using unified diagnostic criteria. by Gerike P.B., Gericke B.L.

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MGL-YOLO: A Lightweight Barcode Target Detection Algorithm by Yuanhao Qu, Fengshou Zhang

“…Due to the critical importance of one-dimensional barcode detection in logistics, retail, and manufacturing, which has become a key issue affecting operational efficiency, researchers have shown increasing interest in this area. …”
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Study on GPR Image Restoration for Urban Complex Road Surfaces Using an Improved CycleGAN by Xinxin Huang, Jialin Liu, Feng Yang, Xu Qiao, Liang Gao, Tingyang Fu, Jianshe Zhao

“…In urban road detection using Ground Penetrating Radar (GPR), challenges arise from complex and variable road structures and diversified detection environments. …”
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Unsupervised selective labeling for semi-supervised industrial defect detection by Jian Ge, Qin Qin, Shaojing Song, Jinhua Jiang, Zhiwei Shen

“…This has motivated a shift towards semi-supervised learning (SSL), which leverages labeled and unlabeled data to improve learning efficiency and reduce annotation costs. This work proposes the unsupervised spectral clustering labeling (USCL) method to optimize SSL for industrial challenges like defect variability, rarity, and complex distributions. …”
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Underwater Object Detection Algorithm Based on an Improved YOLOv8 by Fubin Zhang, Weiye Cao, Jian Gao, Shubing Liu, Chenyang Li, Kun Song, Hongwei Wang

“…Due to the complexity and diversity of underwater environments, traditional object detection algorithms face challenges in maintaining robustness and detection accuracy when applied underwater. …”
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Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest by Gabriel R. Pardini, Paulo M. Tasinaffo, Elcio H. Shiguemori, Tahisa N. Kuck, Marcos R. O. A. Maximo, William R. Gyotoku

“…By addressing the limitations identified in the initial approach, this refined algorithm aims to improve detection accuracy and operational efficiency in complex rainforest environments. …”
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Green Apple Detector Based on Optimized Deformable Detection Transformer by Qiaolian Liu, Hu Meng, Ruina Zhao, Xiaohui Ma, Ting Zhang, Weikuan Jia

“…However, due to the complex orchard environment, the ever-changing posture of the target fruit, and the difficulty of detecting green target fruit similar to the background, they bring new challenges to the detection of green target fruit. …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…This study pioneers an innovative framework, using Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, combined with a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, to enhance the detection of DR. Inspired by principles of randomization-based learning, our approach incorporates elements of stochastic modeling within the CNN-RBF architecture to optimize feature extraction and classification, mirroring the efficiency of non-iterative training processes. …”
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Research on Surface Defect Detection of Rare-Earth Magnetic Materials Based on Improved SSD by Bin Zhang, Shuqi Fang, Zhixi Li

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Lightweight highland barley detection based on improved YOLOv5 by Minghui Cai, Hui Deng, Jianwei Cai, Weipeng Guo, Zhipeng Hu, Dongzheng Yu, Houxi Zhang

“…In addition,the proposed model outperformed mainstream object detection algorithms such as Faster R-CNN, Mask R-CNN, RetinaNet, YOLOv7, and YOLOv8, in terms of detection accuracy and computational efficiency. …”
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Design and Implementation of an Assistive Real-Time Red Lionfish Detection System for AUV/ROVs by M-Mahdi Naddaf-Sh, Harley Myler, Hassan Zargarzadeh

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DCFE-YOLO: A novel fabric defect detection method. by Lei Zhou, Bingya Ma, Yanyan Dong, Zhewen Yin, Fan Lu

“…However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. …”
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Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security by Hanadi Hakami, Muhammad Faheem, Majid Bashir Ahmad

“…We compared several ML and DL techniques to detect and address the most efficient one for our pipeline. …”
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Implementation of YOLOv7 Model for Human Detection in Difficult Conditions by Arijal B, Andi Sunyoto, M. Hanafi

“…The rapid development of artificial intelligence technology in recent decades has led to the development of highly efficient object detection algorithms, including human detection under difficult conditions. …”
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Damage Localization of Piles Based on Complex Continuous Wavelet Transform: Numerical Example and Experimental Verification by Wenting Zheng, Sifan Wang, Chengxu Lin, Xianying Yu, Jingliang Liu

“…A new signal processing method called complex continuous wavelet transform (CCWT) is introduced in this paper to localize pile damage because it clearly reveals inherent characteristics of response signals. …”
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Building Surface Defect Detection Based on Improved YOLOv8 by Xiaoxia Lin, Yingzhou Meng, Lin Sun, Xiaodong Yang, Chunwei Leng, Yan Li, Zhenyu Niu, Weihao Gong, Xinyue Xiao

“…In intelligent building, efficient surface defect detection is crucial for structural safety and maintenance quality. …”
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YOLOv11-HRS: An Improved Model for Strawberry Ripeness Detection by Jianhua Liu, Jing Guo, Suxin Zhang

“…Automated ripeness detection in large-scale strawberry cultivation is often challenged by complex backgrounds, significant target scale variation, and small object size. …”
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Method for Detecting Tiny Defects on Machined Surfaces of Mechanical Parts Based on Object Recognition by Haotian Li, Zhen Wang, Lipeng Qiu, Xichu Wei

“…Practical results demonstrate that this method outperforms traditional approaches in terms of missed detection rates and detection efficiency, effectively addressing the challenge of detecting complex machining surface defects, and providing a high-precision, high-efficiency defect detection solution for the mechanical part machining field.…”
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SARE: an optimized method for detection functional regulatory elements in genome by Mohammadjavad Hosseinpoor

“…This is primarily due to the complexity of health data, particularly genomic and epigenetic datasets. …”
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AI in dermatology: a comprehensive review into skin cancer detection by Kavita Behara, Ernest Bhero, John Terhile Agee

“…Background Artificial Intelligence (AI) is significantly transforming dermatology, particularly in early skin cancer detection and diagnosis. This technological advancement addresses a crucial public health issue by enhancing diagnostic accuracy, efficiency, and accessibility. …”
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