Research on SeaTreasure Target Detection Technology Based on Improved YOLOv7-Tiny by Xiang Shi, Yunli Zhao, Jinrong Guo, Yan Liu, Yongqi Zhang

“…To address this challenge, this paper proposes a target detection algorithm for underwater sea treasures called UPA-YOLO, which aims to achieve accurate and efficient detection of underwater treasures and accelerates the inference through model transformation to enable the deployment of the detection model in edge devices. …”
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Fast Quality Detection of <i>Astragalus</i> Slices Using FA-SD-YOLO by Fan Zhao, Jiawei Zhang, Qiang Liu, Chen Liang, Song Zhang, Mingbao Li

“…Additionally, the integration of the SD module into the detection head optimizes parameter efficiency while improving detection performance. …”
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EGRN-YOLO: An Enhanced Multi-View Remote Sensing Detection Algorithm for Onshore Wind Turbines Based on YOLOv7 by Renzheng Xue, Haiqiang Xu, Qianlong Wu

“…Wind turbines, as the core components of wind power generation systems, play a crucial role in determining the overall generation efficiency and operational safety. However, the challenges posed by complex backgrounds, significant variations in the scale of wind turbine targets, and arbitrary orientations in unmanned aerial vehicle (UAV) remote sensing images have significantly increased the difficulty of real-time wind turbine detection. …”
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Detection of Surface Defects in Steel Based on Dual-Backbone Network: MBDNet-Attention-YOLO by Xinyu Wang, Shuhui Ma, Shiting Wu, Zhaoye Li, Jinrong Cao, Peiquan Xu

“…Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. …”
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DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks by Wasim Khan, Mohammad Haroon, Ahmad Neyaz Khan, Mohammad Kamrul Hasan, Asif Khan, Umi Asma Mokhtar, Shayla Islam

“…In this paper, we propose a new framework called DVAEGMM to detect anomalies on attributed networks. First, our framework utilizes a dual variational autoencoder for capturing the complex cross-modality relationships between node attributes and network structure, like vanilla autoencoders, but it also considers the potential data distribution and makes use of a generative adversarial network (GAN) for an adversarial regularization approach. …”
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A High-Accuracy Underwater Object Detection Algorithm for Synthetic Aperture Sonar Images by Jiahui Su, Deyin Xu, Lu Qiu, Zhiping Xu, Lixiong Lin, Jiachun Zheng

“…Compared with YOLOv8s, the proposed HAUOD algorithm can achieve <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.2</mn><mo>%</mo></mrow></semantics></math></inline-formula> higher accuracy with only <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>50.4</mn><mo>%</mo></mrow></semantics></math></inline-formula> model size, and reduce the computational complexity by half. Moreover, the HAUOD method exhibits significant advantages in balancing computational efficiency and accuracy compared to mainstream detection models.…”
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Classification of SERS spectra for agrochemical detection using a neural network with engineered features by Mateo Frausto-Avila, Monserrat Ochoa-Elias, Jose Pablo Manriquez-Amavizca, María del Carmen González-López, Gonzalo Ramírez-García, Mario Alan Quiroz-Juárez

“…Compared to other machine-learning algorithms, our approach offers reduced computational complexity while maintaining or exceeding the accuracy of more complex models. …”
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Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model by Yuan Liu, Yilong Liu, Xiaoyan Guo, Xi Ling, Qingyi Geng

“…On the AL10-DET dataset, SLF-YOLO achieves a mAP of 86.8%, striking an effective balance between detection accuracy and computational efficiency without increasing model complexity. …”
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Efficient Cow Body Condition Scoring Using BCS-YOLO: A Lightweight, Knowledge Distillation-Based Method by Zhiqiang Zheng, Zhuangzhuang Wang, Zhi Weng

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Real-time detection of Chinese cabbage seedlings in the field based on YOLO11-CGB by Hang Shi, Hang Shi, Changxi Liu, Changxi Liu, Miao Wu, Miao Wu, Hui Zhang, Hui Zhang, Hang Song, Hang Song, Hao Sun, Hao Sun, Yufei Li, Yufei Li, Jun Hu, Jun Hu

“…The model’s outputs are visualized using a heat map, and an Average Temperature Weight (ATW) metric is introduced to quantify the heat map’s effectiveness.Results and discussionComparative analysis reveals that YOLO11-CGB outperforms established object detection models like Faster R-CNN, YOLOv4, YOLOv5, YOLOv8 and the original YOLO11 in detecting Chinese cabbage seedlings across varied heights, angles, and complex settings. …”
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Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion by Chun Liu, Yuanliang Zhang, Jingfu Shen, Feiyue Liu

“…However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce the contrast between the target and the background. As a result, detecting infrared targets in complex marine environments remains challenging. …”
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RGE-YOLO enables lightweight road packaging bag detection for enhanced driving safety by Dangfeng Pang, Zhiwei Guan, Tao Luo, Yanhao Liang, Ruzhen Dou

“…However, research on detecting road packaging bags remains limited, and existing object detection models face challenges in small object detection, computational efficiency, and embedded deployment. …”
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MSAN-Net: An End-to-End Multi-Scale Attention Network for Universal Industrial Defect Detection by Zelu Wang, Ming Luo, Xinghe Xie, Yue Sun, Xinyu Tian, Zhengxuan Chen, Junwei Xie, Qinquan Gao, Tong Tong, Yue Liu, Tao Tan

“…Traditional manual visual inspection or single-task deep learning models were often struggled to balance detection efficiency and accuracy in complex industrial scenarios. …”
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An improved multi‐scale YOLOv8 for apple leaf dense lesion detection and recognition by Shixin Huo, Na Duan, Zhizheng Xu

“…Abstract Apple leaf lesions present a challenge for their detection and recognition because of their wide variety of species, morphologies, uneven sizes, and complex backgrounds. …”
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An Economic Analysis of the Interaction between Third-Party Liability for Breach of Contract and Specific Performance: A Comparative Study of United States and Iranian Law by Hasan Alipour, Ahad Shahi Damanjani

“…The findings underscore the complexities involved in reconciling these two legal remedies, particularly from the perspective of economic efficiency and justice.     …”
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Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks by Wasim Khan, Afsaruddin Mohd, Mohammad Suaib, Mohammad Ishrat, Anwar Ahamed Shaikh, Syed Mohd Faisal

“…This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism, thereby creating a more nuanced and efficient method for anomaly detection in complex networks. …”
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Enhanced YOLOv5s Model for Improved Multi-Sized Object Detection in Road Scenes by Sangavi Sivanandham, Dharanibai Gunaseelan

“…Detecting objects in complex driving environments is crucial for autonomous vehicles to navigate safely. …”
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An Improved Unmanned Aerial Vehicle Forest Fire Detection Model Based on YOLOv8 by Bensheng Yun, Xiaohan Xu, Jie Zeng, Zhenyu Lin, Jing He, Qiaoling Dai

“…Taking into account efficiency and cost-effectiveness, deep-learning-driven UAV remote sensing fire detection algorithms have emerged as a favored research trend and have seen extensive application. …”
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TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection by Rijun Wang, Rijun Wang, Yesheng Chen, Fulong Liang, Xiangwei Mou, Xiangwei Mou, Guanghao Zhang, Hao Jin

“…Current tomato leaf disease detection methods, however, encounter challenges in extracting multi-scale features, identifying small targets, and mitigating complex background interference. …”
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Research on the detection of obstacles in front of unmanned vehicles in opencast mines based on binocular vision by Shunling RUAN, Huiguo ZHANG, Qinghua GU, Caiwu LU, Di LIU, Jing MAO

“…Driverless cars in the complex environment of the open pit mining area encountered falling rocks, puddles, pedestrians and other obstacles have great safety hazards, easy to cause vehicle rollover, stuck in the car, resulting in the risk of loss of property or pose a threat to the safety of personnel, therefore, the complex and changing terrain environment on the road of the open pit mine as an important problem solving of the open pit mine unmanned vehicles in the mining intelligence, the need to measure the depth of obstacles in front of the car of the while guaranteeing the accuracy and speed of obstacle detection. …”
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