Efficient wildlife monitoring: Deep learning-based detection and counting of green turtles in coastal areas by Naoya Noguchi, Hideaki Nishizawa, Taro Shimizu, Junichi Okuyama, Shohei Kobayashi, Kazuyuki Tokuda, Hideyuki Tanaka, Satomi Kondo

“…However, accurately detecting animals remains challenging, especially in complex field environments. …”
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Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11 by Fangyu Hu, Mairheba Abula, Di Wang, Xuan Li, Ning Yan, Qu Xie, Xuedong Zhang

“…By integrating a medical image segmentation model, it effectively tackles challenges including complex background interference, the missed detection of small targets, and restricted generalization ability. …”
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Distinguishing Difficulty Imbalances in Strawberry Ripeness Instances in a Complex Farmland Environment by Yang Gan, Xuefeng Ren, Huan Liu, Yongming Chen, Ping Lin

“…The existing strawberry ripeness detection algorithm has the problems of a low precision and a high missing rate in real complex scenes. …”
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Reliable and efficient magnetic data inversion for resource detection using a hybrid bat algorithm by Khalid S. Essa, Mahmoud Elhussein, Omar A. Gomaa, Zein E. Diab

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Enhancing Real-Time Road Object Detection: The RD-YOLO Algorithm With Higher Precision and Efficiency by Weijian Wang, Wei Yu

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CSCP-YOLO: A Lightweight and Efficient Algorithm for Real-Time Steel Surface Defect Detection by Chenglong Wang, Heng Wang, Yimin Jiang, Lei Yu, Xueting Wang

“…Steel surface defect detection is critical for ensuring product quality, yet remains challenging due to multi-scale defects, small target prevalence, and complex background interference. …”
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YOLO-Air: An Efficient Deep Learning Network for Small Object Detection in Drone-Based Imagery by Jigang Qiu, Fangkai Cai, Ning Fu, Yuanfei Yao

“…However, it poses unique challenges for object detection due to small objects, complex backgrounds, and noise interference. …”
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Lightweight patch-level attention for efficient pig behavior detection: A novel dataset and approach by Shuo Wan, Zhongqiang Huang, Tao Han, Ying Sha

“…The pig farming industry plays a crucial role in food production system. Pig behavior detection facilitates timely anomaly detection, enhances farming efficiency, ensures animal welfare, and prevents diseases. …”
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Efficient fault diagnosis in rolling bearings lightweight hybrid model by Peng Yang, Bozheng Zhang, Jianda Zhao

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Efficient removal of estradiol using MnFe2O4 microsphere and potassium persulfate complex salt by Weiwei Yu, Ting Ai, Weizhe Sun, Shuo Yang, Yufeng Mao, JiangLin Tan, Feng Yu, Chen Yin, Qin Jiang, Chengye Yu, Shiling Chen

“…Abstract In this study, MnFe2O4 microspheres were synthesized to activate potassium persulfate complex salt (Oxone) for the degradation of 17β-estradiol (17β-E2) in aqueous solutions. …”
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Real-time and resource-efficient banana bunch detection and localization with YOLO-BRFB on edge devices by Shuo Wang, Shuo Wang, Shuo Wang, Lijiao Wei, Danran Zhang, Danran Zhang, Ling Chen, Ling Chen, Weihua Huang, Dongjie Du, Kangmin Lin, Zhenhui Zheng, Zhenhui Zheng, Zhenhui Zheng, Jieli Duan

“…Current methods face challenges in achieving high detection accuracy and efficient deployment due to their structural complexity and significant computational demands. …”
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RiceNet: Efficient CNN for High-Throughput Image-Based Rice Panicle Detection and Counting by Kushwaha Ragini, Balkrishna Sutar Manisha

“…Traditionally, therefore, accurate and efficient detection and counting of rice panicles are labor intensive. …”
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Efficient automated detection of power quality disturbances using nonsubsampled contourlet transform & PCA-SVM by Pampa Sinha, Kaushik Paul, Asit Mohanty, IM Elzein, Chandra Sekhar Mishra, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Ahmed Althobaiti, Hany S Hussein, Thamer AH Alghamdi, Ahmed M Ewais

“…The increasing complexity of modern power systems, driven by smart grid technologies, dispersed generation, and renewable energy sources, has made power quality (PQ) evaluation more challenging. …”
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Accelerated and Energy-Efficient Galaxy Detection: Integrating Deep Learning with Tensor Methods for Astronomical Imaging by Humberto Farias, Guillermo Damke, Mauricio Solar, Marcelo Jaque Arancibia

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Can Separation Enhance Fusion? An Efficient Framework for Target Detection in Multimodal Remote Sensing Imagery by Yong Wang, Jiexuan Jia, Rui Liu, Qiusheng Cao, Jie Feng, Danping Li, Lei Wang

“…Leveraging this framework, we present Sep-Fusion—an efficient target detection approach tailored for multimodal remote sensing aerial imagery. …”
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Camera systems and their user recognition reliability when entering an agri-food complex by Jaroslav Mrázek, Jakub Vošáhlík, Eva Olmrová, Martin Pexa, Zdeněk Aleš, Jakub Čedík

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Time Series Foundation Model for Improved Transformer Load Forecasting and Overload Detection by Yikai Hou, Chao Ma, Xiang Li, Yinggang Sun, Haining Yu, Zhou Fang

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DEPDet: A Cross-Spatial Multiscale Lightweight Network for Ship Detection of SAR Images in Complex Scenes by Jing Zhang, Fan Deng, Yonghua Wang, Jie Gong, Ziyang Liu, Wenjun Liu, Yinmei Zeng, Zeqiang Chen

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MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection by Jingcui Ma, Nian Pan, Dengyu Yin, Di Wang, Jin Zhou

“…Infrared small-target detection encounters significant challenges due to a low image signal-to-noise ratio, limited target size, and complex background noise. …”
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A Lightweight Model for Shine Muscat Grape Detection in Complex Environments Based on the YOLOv8 Architecture by Changlei Tian, Zhanchong Liu, Haosen Chen, Fanglong Dong, Xiaoxiang Liu, Cong Lin

“…This study addresses these challenges by proposing a lightweight YOLOv8-based model, incorporating DualConv and the novel C2f-GND module to enhance feature extraction and reduce computational complexity. Evaluated on the newly developed Shine-Muscat-Complex dataset of 4715 images, the proposed model achieved a 2.6% improvement in mean Average Precision (mAP) over YOLOv8n while reducing parameters by 36.8%, FLOPs by 34.1%, and inference time by 15%. …”
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