Double-layer membrane framework-based gold microelectrode for determination of natural labile copper in complex water environments by Xinyue Hu, Haitao Han, Shanshan Wang, Dawei Pan

“…However, the determination of low concentration labile Cu (CuLabile) in complex water environments remains a huge challenge. …”
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Lightweight Small Target Detection Algorithm Based on YOLOv8 Network Improvement by Xiaoyi Hao, Ting Li

“…The modules have been designed to optimise feature extraction and improve model efficiency. The paper also discusses the challenges associated with low accuracy in small target detection and high model complexity in UAV applications. …”
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Quantum Edge Detection and Convolution Using Paired Transform-Based Image Representation by Artyom Grigoryan, Alexis Gomez, Sos Agaian, Karen Panetta

“…Classical edge detection algorithms often struggle to process large, high-resolution image datasets efficiently. …”
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Application of the YOLOv11-seg algorithm for AI-based landslide detection and recognition by Luhao He, Yongzhang Zhou, Lei Liu, Yuqing Zhang, Jianhua Ma

“…Compared with traditional methods, YOLOv11-seg performs better in detecting complex boundaries and handling occlusion, demonstrating superior detection accuracy and segmentation quality. …”
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Pear Fruit Detection Model in Natural Environment Based on Lightweight Transformer Architecture by Zheng Huang, Xiuhua Zhang, Hongsen Wang, Huajie Wei, Yi Zhang, Guihong Zhou

“…This model provides technical support for Xinli No. 7 fruit detection and model deployment in complex environments.…”
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YOLO-MES: An Effective Lightweight Underwater Garbage Detection Scheme for Marine Ecosystems by Chengxu Huang, Wenyuan Zhang, Beitian Zheng, Jiahao Li, Bochen Xie, Ruisi Nan, Zongming Tan, Baohua Tan, Neal N. Xiong

“…Experimental results indicate that YOLO-MES achieves 95.8% accuracy on the dataset, while reducing model size and computational complexity by 64% and 67%, respectively. Compared to existing mainstream detection algorithms, YOLO-MES offers significant advantages in lightweight design and computational efficiency, providing a practical and deployable solution for underwater target detection on mobile devices.…”
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Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process by Aron Csato, Florin Mariasiu

“…An important aspect of this new architecture is the balance between performance and computational efficiency, maintaining reasonable values of performance parameters with a competitive inference time. …”
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Self-Supervised Drift-Resilient Classification for Time Series Industrial Anomaly Detection by Myung-Kyo Seo, Byeong Hoon Yoon, Junseung Ryu, Hyung Ju Hwang

“…In modern industrial environments, early detection of anomalies is essential to prevent unplanned downtime and maintain operational efficiency. …”
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A hybrid Bi-LSTM and RBM approach for advanced underwater object detection. by Manimurugan S, Karthikeyan P, Narmatha C, Majed M Aborokbah, Anand Paul, Subramaniam Ganesan, Rajendran T, Mohammad Ammad-Uddin

“…This research addresses the imperative need for efficient underwater exploration in the domain of deep-sea resource development, highlighting the importance of autonomous operations to mitigate the challenges posed by high-stress underwater environments. …”
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A Hybrid and Modular Integration Concept for Anomaly Detection in Industrial Control Systems by Christian Goetz, Bernhard G. Humm

“…However, the direct integration of anomaly detection within such a system is complex due to the wide variety of hardware used, different communication protocols, and given industrial requirements. …”
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Seedling Stage Corn Line Detection Method Based on Improved YOLOv8 by LI Hongbo, TIAN Xin, RUAN Zhiwen, LIU Shaowen, REN Weiqi, SU Zhongbin, GAO Rui, KONG Qingming

“…However, traditional detection methods struggle to maintain high accuracy and efficiency under challenging conditions, such as strong light exposure and weed interference. …”
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A Reparameterization Feature Redundancy Extract Network for Unmanned Aerial Vehicles Detection by Shijie Zhang, Xu Yang, Chao Geng, Xinyang Li

“…In unmanned aerial vehicles (UAVs) detection, challenges such as occlusion, complex backgrounds, motion blur, and inference time often lead to false detections and missed detections. …”
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Research on object detection and recognition in remote sensing images based on YOLOv11 by Lu-hao He, Yong-zhang Zhou, Lei Liu, Wei Cao, Jian-hua Ma

“…Abstract This study applies the YOLOv11 model to train and detect ground object targets in high-resolution remote sensing images, aiming to evaluate its potential in enhancing detection accuracy and efficiency. …”
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Real-time classroom student behavior detection based on improved YOLOv8s by Xiaojing Sheng, Suqiang Li, Sixian Chan

“…However, the field still faces specific challenges, primarily concerning the accuracy of identifying student behaviors within complex and variable classroom environments, as well as the real-time capabilities of detection algorithms. …”
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A recurrent YOLOv8-based framework for event-based object detection by Diego A. Silva, Kamilya Smagulova, Ahmed Elsheikh, Mohammed E. Fouda, Ahmed M. Eltawil

“…The results demonstrate the significant potential of bio-inspired event-based vision sensors when combined with advanced object detection frameworks. In particular, the ReYOLOv8 system effectively bridges the gap between biological principles of vision and artificial intelligence, enabling robust and efficient visual processing in dynamic and complex environments. …”
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A policy conflict detection mechanism for multi-controller software-defined networks by You Lu, Qiming Fu, Xuefeng Xi, Zhenping Chen, Encen Zou, Baochuan Fu

“…The experimental results under the campus network environment prove that our method can effectively detect the conflict of flow policies existing in the multi-controller software-defined network and has advantages over the existing methods in the integrity, accuracy, and efficiency of the detection.…”
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Advances in research on novel technologies for the detection of exogenous contaminants in traditional Chinese medicine by Ziyu Guo, Junyao Li, Lina Zeng, Ping Wang, Meifang Li, Chang Su, Shuhong Wang

“…Exogenous contaminants in traditional Chinese medicine (TCM), including pesticide residues, heavy metals, mycotoxins, and sulfur dioxide residues, pose significant risks to human health and environmental safety. Conventional detection methods are limited by insufficient sensitivity, complex sample preparation, and challenges in multi-residue analysis, compromising accuracy and efficiency. …”
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Single-Frame Infrared Target Detection Based on Fast Content-Related Modeling by Zipeng Zhang, Xidong Zhao, Wenzheng Wang, Yuqi Han, Chenwei Deng, Zhuokai Li, Linbo Tang

“…Most of methods mainly concentrate on modeling global features, overlooking the variations in local features due to complex scenes. To solve these problems, a single-frame infrared target detection method based on fast content-related modeling is proposed to combine global and local features of infrared images, describing the common features of varying scenes robustly and enhancing the distinction between targets and backgrounds. …”
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An optimized stacking-based TinyML model for attack detection in IoT networks. by Anshika Sharma, Shalli Rani, Mohammad Shabaz

“…To address these challenges, a stacking-based Tiny Machine Learning (TinyML) models has been proposed for attack detection in IoT networks. This ensures detection efficiently and without additional computational overhead. …”
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Rapid detection and quantification of Nile Red-stained microplastic particles in sediment samples by Masashi Tsuchiya, Tomo Kitahashi, Yosuke Taira, Hitoshi Saito, Kazumasa Oguri, Ryota Nakajima, Dhugal J. Lindsay, Katsunori Fujikura

“…This is especially the case for deep-sea sediments, where the particle sizes are small and pretreatment processes are complex and time-consuming. To address the need for rapid and efficient detection of MPs, we propose a novel method for automatically identifying and counting Nile Red (NR)-stained sedimentary MP particles captured under a stereoscopic fluorescence microscope. …”
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