An enhanced light weight face liveness detection method using deep convolutional neural network by Swapnil R. Shinde, Anupkumar M. Bongale, Deepak Dharrao, Sudeep D. Thepade

“…The methodology proposed in the article has the following key contributions: • Design of Light Weight Dual Stream CNN architecture with a parallel dropout layer to minimize over fitting issue. • Design of Generalized and Robust deep CNN architecture that detects both 2D and 3D attacks with higher efficiency compared to existing methodology. • Method validation done with State-of-the-Art methods using the standard performance metrics for face spoofing attack detection.…”
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Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers by Anupam Yadav, Rifat Hussain, Madhu Shukla, Jayaprakash B, Rishiv Kalia, S. Prince Mary, Chou-Yi Hsu, Manoj Kumar Mishra, Kashif Saleem, Mohammed El-Meligy

“…Despite minor overfitting, SPBO’s efficiency makes it a cost-effective solution. In general, our findings contribute to the advancement of driver monitoring systems and road safety while emphasizing the broader role of meta-heuristic techniques in deep learning optimization.…”
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Trade-offs between machine learning and deep learning for mental illness detection on social media by Zhanyi Ding, Zhongyan Wang, Yeyubei Zhang, Yuchen Cao, Yunchong Liu, Xiaorui Shen, Yexin Tian, Jianglai Dai

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YOLOv10-LGDA: An Improved Algorithm for Defect Detection in Citrus Fruits Across Diverse Backgrounds by Lun Wang, Rong Ye, Youqing Chen, Tong Li

“…Additionally, we introduce the GFPN module to strengthen multi-scale information interaction through cross-scale feature fusion, thereby improving detection accuracy for small-target diseases. The incorporation of the DAT mechanism is designed to achieve higher efficiency and accuracy in handling complex visual tasks. …”
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Lightweight detection and segmentation of crayfish parts using an improved YOLOv11n segmentation model by Wei Shi, Jun Zhang, YunFan Fu, DanWei Chen, JianPing Zhu, ChunFeng Lv

“…Abstract Crayfish sub-part processing requires high precision and efficiency in complex environments; however, current methods often fail to effectively handle small anatomical structures and are unsuitable for edge-device deployment. …”
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Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures by Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

“…Automated pavement crack detection faces significant challenges due to the complex shapes of crack patterns, their similarity to non-crack textures, and varying environmental conditions such as lighting and noise. …”
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Precise amplification-free detection of highly structured RNA with an enhanced SCas12a assay by Junqi Zhang, Wenhao Yin, Qingyuan Jiang, Wuxiang Mao, Wenwen Deng, Shuqi Jin, Xinping Wang, Ruyi He, Jie Qiao, Yi Liu

“…Abstract The CRISPR/Cas12a system has revolutionized molecular diagnostics, yet the direct detection of RNA, particularly those with complex structures, remains a significant challenge. …”
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YOLO11m-SCFPose: An Improved Detection Framework for Keypoint Extraction in Cucumber Fruit Phenotyping by Huijiao Yu, Xuehui Zhang, Jun Yan, Xianyong Meng

“…To address the issues of low efficiency and large errors in traditional manual cucumber fruit phenotyping methods, this paper proposes the application of keypoint detection technology for cucumber phenotyping and designs an improved lightweight model called YOLO11m-SCFPose. …”
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An Integrated CNN-BiLSTM-Transformer Framework for Improved Anomaly Detection Using Surveillance Videos by Sarfaraz Natha, Mohammad Siraj, Fareed Ahmed, Majid Altamimi, Muslim Syed

“…Close Circuits Television (CCTV) Cameras are widely used to surveillance normal and anomalous incidents. Real-world anomaly detection in complex environment situations is a significant challenge due to its diverse nature manually reviewing this vast data is difficult, time-consuming, and prone to errors. …”
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Two-Stage Target Detection for Compact HFSWR With Space-to-Depth YOLOv8 and Multiframe ViT by Tong Wu, Ming Li, Jiong Niu, Ling Zhang, Wandong Zhang, Qing Ming Jonathan Wu

“…These results confirm that MFTDF delivers superior detection accuracy and efficiency, offering a robust solution for maritime target detection in complex scenarios.…”
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Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment by Alanoud Al Mazroa, Fahad R. Albogamy, Mohamad Khairi Ishak, Samih M. Mostafa

“…By leveraging the integral ability of DL methods to learn complex representations, this technique enhances the accuracy and efficiency of detecting diverse and growing cyber-attacks. …”
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A dual-domain perception gate-controlled adaptive fusion algorithm for road crack detection by Ziyang Zhang, Yong’an Feng

“…Abstract Road crack detection presents critical challenges, including diverse defect patterns and complex anomaly characteristics. …”
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LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach by Mingxin Liu, Mingxin Liu, Yujie Wu, Ruixin Li, Cong Lin, Cong Lin

“…However, traditional underwater object detection methods struggle to achieve accurate detection in complex underwater environments with limited computational resources. …”
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Lightweight coal miners and manned vehicles detection model based on deep learning and model compression techniques: A case study of coal mines in Guizhou region by Beijing XIE, Heng LI, Zheng LUAN, Zhen LEI, Xiaoxu LI, Zhuo LI

“…Balancing between model detection performance and efficiency poses many challenges. …”
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Enhancing Human Detection in Occlusion-Heavy Disaster Scenarios: A Visibility-Enhanced DINO (VE-DINO) Model with Reassembled Occlusion Dataset by Zi-An Zhao, Shidan Wang, Min-Xin Chen, Ye-Jiao Mao, Andy Chi-Ho Chan, Derek Ka-Hei Lai, Duo Wai-Chi Wong, James Chung-Wai Cheung

“…Natural disasters create complex environments where effective human detection is both critical and challenging, especially when individuals are partially occluded. …”
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Star-YOLO: A Lightweight Real-Time Wheat Grain Detection Model for Embedded Deployment by Zhihang Qu, Xiao Liang, Sicheng Liang, Xiumei Guo

“…With the rapid advancement of precision agriculture, traditional object detection algorithms struggle with limited efficiency and accuracy in wheat grain detection and counting, while the need for real-time deployment of deep learning models on embedded devices becomes increasingly critical. …”
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Intelligent single-cell manipulation: LLMs- and object detection-enhanced active-matrix digital microfluidics by Zhiqiang Jia, Chen Jiang, Jiahao Li, Yacine Belgaid, Mingfeng Ge, Li Li, Siyi Hu, Xing Huang, Tsung-Yi Ho, Wenfei Dong, Zhiwen Yu, Hanbin Ma

“…By combining this with a fully programmable lab-on-a-chip system, we present a breakthrough for SCSM by combining LLMs and object detection technologies. With the proposed platform, the single-cell sample generation rate and identification precision reach up to 25% and 98%, respectively, which are much higher than the existing platforms in terms of SCSM efficiency and performance. …”
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Surface Damage Detection in Hydraulic Structures from UAV Images Using Lightweight Neural Networks by Feng Han, Chongshi Gu

“…Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. …”
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Detection of Stator Faults in Three-Phase Induction Motors Using Stray Flux and Machine Learning by Ailton O. Louzada, Wesley A. Souza, Avyner L. O. Vitor, Marcelo F. Castoldi, Alessandro Goedtel

“…Despite advancements in non-invasive sensing, challenges remain in balancing fault detection accuracy, computational efficiency, and adaptability to real-world conditions. …”
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MA-YOLO: A Pest Target Detection Algorithm with Multi-Scale Fusion and Attention Mechanism by Yongzong Lu, Pengfei Liu, Chong Tan

“…Agricultural pest detection is critical for crop protection and food security, yet existing methods suffer from low computational efficiency and poor generalization due to imbalanced data distribution, minimal inter-class variations among pest categories, and significant intra-class differences. …”
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