A lightweight algorithm for steel surface defect detection using improved YOLOv8 by Shuangbao Ma, Xin Zhao, Li Wan, Yapeng Zhang, Hongliang Gao

“…Finally, the SIoU (Simplified IoU ) is used to replace the traditional CIoU loss function, which can make the anchor frame more fast and accurate in the regression process, to improve the stability and the robustness of detection. …”
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SurfaceVision: An Automated Module for Surface Fault Detection in 3D Printed Products by Laukesh Kumar, Manoj Kumar Satyarthi

“…The traditional method currently requires visual information processing devices or continuous monitoring of the process via a camera, which is very resource consuming and costly. Machine learning techniques being used for automatic detection of the faults suffer in real time conditions with inefficient fault detection due to the inability of adaptation to real time changes in the printing process. …”
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Employing SAE-GRU deep learning for scalable botnet detection in smart city infrastructure by Usman Tariq, Tariq Ahamed Ahanger

“…These findings enhance the understanding of IoT security by offering a scalable and resource-efficient solution for botnet detection. The functional investigation establishes a foundation for future research into adaptive security mechanisms that address emerging threats and highlights the practical potential of advanced deep learning techniques in safeguarding next-generation smart city ecosystems.…”
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Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny by Yuyang Miao, Xihan Wang, Ning Zhang, Kai Wang, Lianhe Shao, Quanli Gao

“…The algorithm’s performance in handling object occlusion and multi-scale detection is enhanced by introducing the VarifocalLoss loss function and improving the feature fusion network to BiFPN. …”
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GESC-YOLO: Improved Lightweight Printed Circuit Board Defect Detection Based Algorithm by Xiangqiang Kong, Guangmin Liu, Yanchen Gao

“…Simultaneously, the model size is reduced by 25.4%, the parameter count is cut down by 28.6%, and the computational resource consumption is reduced by 26.8%. This successfully achieves the harmonization of detection precision and model lightweighting.…”
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RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images by Yansong Wang, Xuanxi Yang, Haoyu Wang, Huihua Wang, Zaiqing Chen, Lijun Yun

“…Furthermore, to optimize the detection performance under hardware resource constraints, we apply knowledge distillation to RSWD-YOLO, thereby further improving the detection accuracy. …”
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Automatic Detection and Identification of Underdense Meteors Based on YOLOv8n-BP Model by Siyuan Chen, Guobin Yang, Chunhua Jiang, Tongxin Liu, Xuhui Liu

“…Utilizing the Fresnel oscillation properties of meteor echoes, a BP network based on a Gaussian activation function is designed in this paper to enable it to detect meteor head and tail positions more accurately. …”
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YOLOLS: A Lightweight and High-Precision Power Insulator Defect Detection Network for Real-Time Edge Deployment by Qinglong Wang, Zhengyu Hu, Entuo Li, Guyu Wu, Wengang Yang, Yunjian Hu, Wen Peng, Jie Sun

“…However, deploying deep learning models on edge devices presents significant challenges due to limited computational resources and strict latency constraints. To address these issues, we propose YOLOLS, a lightweight and efficient detection model derived from YOLOv8n and optimized for real-time edge deployment. …”
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YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu, Ende Zhang

“…Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard computational resource constraints. …”
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GSF-YOLOv8: A Novel Approach for Fire Detection Using Gather-Distribute Mechanism and SimAM Attention by Caixiong Li, Dali Wu, Xing Zhang, Peng Wu

“…To address the current challenges in fire detection algorithms, including insufficient feature extraction, high computational complexity, limited deployment on resource-constrained devices, missed detections, false detections, and low accuracy, we developed a high-precision algorithm named GSF-YOLOv8. …”
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Certificateless Batch Authentication Scheme and Intrusion Detection Model Based on the Mobile Edge Computing Technology NDN-IoT Environment by Jianzhao Sun

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Rice-SVBDete: a detection algorithm for small vascular bundles in rice stem’s cross-sections by Xiaoying Zhu, Weiyu Zhou, Jianguo Li, Jianguo Li, Mingchong Yang, Mingchong Yang, Haiyu Zhou, Haiyu Zhou, Jiada Huang, Jiada Huang, Jiahua Shi, Jun Shen, Guangyao Pang, Lingqiang Wang, Lingqiang Wang, Lingqiang Wang

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Relationships between vegetation indices and surface reflectance: Implications for detecting and monitoring sandification in arid regions by Yifan Yue, Wenzhi Zhao, Rentao Liu

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Enhanced Intrusion Detection in In-Vehicle Networks Using Advanced Feature Fusion and Stacking-Enriched Learning by Ali Altalbe

“…To address this problem, machine learning (ML) based intrusion detection systems (IDSs) have been proposed. However, existing IDSs suffer from low detection accuracy, limited real-time response, and high resource requirements. …”
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Energy-Efficiency using Critical Nodes Detection Problem in Industrial Wireless Sensor Networks (IWSNs) by Karima MOULEY, Mohamed Amin TAHRAOUI, Abdelaziz KELLA

“…Experiments simulation validates our proposed approach, approving its efficiency in reducing significant energy consumption while preserving connectivity and functionality for industrial systems. Furthermore, the results highlight the potential of using critical node analysis to support sustainable and efficient operations in resource-constrained industrial environments. …”
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Identification and functional analysis of novel precursor genes in cyclic peptide biosynthesis in Pseudostellaria heterophylla by Jiao Xu, Wei Zheng, Xiaohong Ou, Hua He, Changgui Yang, Lanping Guo, Chenghong Xiao, Weike Jiang, Guoping Shu, Tao Zhou

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DECISION TREE WITH HILL CLIMBING ALGORITHM BASED SPECTRUM HOLE DETECTION IN COGNITIVE RADIO NETWORK by N Suganthi, R Meenakshi, A Sairam, M Parvathi

“…The approach integrates a Decision Tree (DT) algorithm for rapid initial classification of Primary User (PU) activity, followed by a Hill Climbing (HC) optimization algorithm that fine-tunes the detection based on a fitness function. Entropy and throughput metrics are employed as decision conditions at each sensing channel, enhancing uncertainty measurement and maintaining detection robustness under low Signal-to-Noise Ratio (SNR) conditions. …”
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DSFA-SwinNet: A Multi-Scale Attention Fusion Network for Photovoltaic Areas Detection by Shaofu Lin, Yang Yang, Xiliang Liu, Li Tian

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TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5 by Han Wang, Zhenwei Yang, Qiaoshou Liu, Qiang Zhang, Honggang Wang

“…Finally, the EIOU loss function is introduced to measure the overlap between the predicted box and the real box more accurately and improve the detection accuracy. …”
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YOLOv8n-DDSW: an efficient fish target detection network for dense underwater scenes by Jinwang Yi, Wei Han, Fangfei Lai

“…Therefore, the YOLOv8n-DDSW fish target detection algorithm was proposed in this article to resolve the detection difficulties resulting from fish occlusion, deformation and detail loss in complex intensive aquaculture scenarios. (1) The C2f-deformable convolutional network (DCN) module is proposed to take the place of the C2f module in the YOLOv8n backbone to raise the detection accuracy of irregular fish targets. (2) The dual-pooling squeeze-and-excitation (DPSE) attention mechanism is put forward and integrated into the YOLOv8n neck network to reinforce the features of the visible parts of the occluded fish target. (3) Small detection is introduced to make the network more capable of sensing small targets and improving recall. (4) Wise intersection over union (IOU) rather than the original loss function is used for improving the bounding box regression performance of the network. …”
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