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检索条件"主题词=detection algorithms"

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Forest Fire Severity detection using AI

Forest Fire Severity Detection using AI

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Sustainable Energy Technologies and Computational Intelligence (SETCOM), International Conference on

作者： Pradip Kumar Barik Jay Suthar Raaj Patel Department of ICT Pandit Deendayal Energy University Gandhinagar India

ISBN: (数字)9798331520540

ISBN: (纸本)9798331520557

Forest fires pose significant threats to ecosystems and contribute to global warming. Assessing fire severity is crucial for effective firefighting and minimizing damage, typically relying on post-fire metrics. This study addresses real-time forest fire severity assessment, crucial for minimizing fire damage. We propose a method combining deep learning and weather data. Fire severity was then estimated by combining fire area (from image analysis), a visual fire distance rating, and the Fire Weather Index (FWI) from weather data. Various optimizing techniques are used on a formulated weighted average equation such as Genetic algorithm, Particle Swarm Optimization, and Quasinewton method (QN) for finding the optimized weights that minimizes the Mean Square Error (MSE) between assigned and calculated severity scores. Interestingly, the FWI received the highest weight, while the fire area received the least, highlighting the importance of weather data alongside image analysis.

关键词： Wildfires Image analysis Forestry Mathematical models Global warming Indexes Artificial intelligence Particle swarm optimization Meteorology Genetic algorithms detection algorithms Artificial intelligence Climate change

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Recent Advances, Challenges and Future Prospects of Multi-Crop Leaf Disease detection Algorithm Using Deep Learning: A Comprehensive Review

Recent Advances, Challenges and Future Prospects of Multi-Cr...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： Swapnil P. Bangal Satish R. Jondhale Balasaheb S. Agarkar Sachin V. Chaudhari Shakil A. Shaikh Department of Electronics & Telecommunication Engineering Sanjivani College of Engineering Kopergoan SPPU Pune India Department of Electronics & Computer Engineering Pravara Rural Engineering College Loni SPPU Pune India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Over the past few years, there have been prominent progress in the development of multi-crop leaf disease detection algorithms using deep learning, offering promising solutions to tackle agricultural challenges. This comprehensive review explores the latest advancements in the field, examining the methodologies and models that have become efficient tools for the early diagnosis of disease across a variety of crops. The review not only highlights the successes but also explains the challenges faced by these algorithms, including issues related to data scarcity, model generalization, and real-world implementation. Furthermore, it presents a forward-looking perspective, discussing the future prospects of this technology, emphasizing the potential for its widespread adoption in precision farming and focusing on the demand for more investigation and innovation. By incorporating recent developments, acknowledging existing challenges, and outlining future directions, this review serves as a valuable tool for academics, professionals, and decision-makers who want to use deep learning to identify leaf diseases across several crops.

关键词： Deep learning Precision agriculture Technological innovation Reviews Collaboration Prediction algorithms Real-time systems Stakeholders detection algorithms Diseases

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A Study on a Linear-Frequency-Modulation Interference detection Algorithm for Single-Antenna Low-Earth-Orbit Signal-of-Opportunity Positioning Receivers

A Study on a Linear-Frequency-Modulation Interference Detect...

引用

Data Science and Computer Application (ICDSCA), IEEE International Conference on

作者： Dongfang Jiang Lihao Yao Zhijun Liu Zheng Wei Yingying Zhao Xuyu Wang Yangjun Wang Huaibin Zhang Donghan He Deyong Xian Heng Wei Li Wang Qinqin Li Bin Fan Wensong Li Xin Huang Beijing Satellite Navigation Center Beijing China School of Electronics and Information Engineering Beihang University Beijing Satellite Navigation Center Beijing China 32011 Troops Beijing China PLA Army Academy of Artillery and Air Defense Hefei China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

Signal-of-Opportunity (SOP) positioning based on Low-Earth-Orbit (LEO) constellation has become a research hotspot. Because of its large quantity, wide spectrum coverage and strong signal power, LEO satellite SOP signal has strong anti-jamming ability. However, there is still little research on anti-jamming technology for LEO satellite SOP signal positioning, especially for LFM interference. At present, it has not been deeply studied at home and abroad. Because of the great difference between the downlink signal of LEO satellite and the signal of Global Navigation Satellite System (GNSS) based on medium and high orbit, the research results of anti-jamming of traditional satellite guidance system can not be directly applied. In this paper, a Wigner-Ville distribution interference detection algorithm based on iterative signal cancellation (WVD-SIC) is proposed for LEO constellation signal with narrow downlink bandwidth and high landing signal-to-noise ratio (SNR). The algorithm eliminates the influence of strong signal power in input signal on Linear Frequency Modulation (LFM) interference detection by iterative fitting, and determines the characteristics of LFM interference signal by least square fitting. Finally, the improved performance of the algorithm is verified by simulation. The results show that compared with traditional algorithms, this method can effectively eliminate single/multi-component LFM interference, and improve the anti-jamming performance of LFM significantly in LEO satellite anti-jamming scene.

关键词： Global navigation satellite system Satellites Fitting Low earth orbit satellites Interference Downlink Iterative algorithms Jamming detection algorithms Signal to noise ratio

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Research on Illegal Behavior detection Algorithm of Underground Mine Workers Based on Improved YOLOv8

Research on Illegal Behavior Detection Algorithm of Undergro...

引用

International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： Nan Liu Gang Huang Daxing Xu Lu Zhang School of Electronic and Information Engineering Anhui Jianzhu University He Fei China School of Electronic and Information Engineering Qu Zhou University Qu Zhou China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

Aiming at the problems of poor detection effect and weak generalization ability of illegal production of workers in mines, a mine worker illegal target detection algorithm based on improved YOLOv8 is proposed. In view of the problem of large scale changes of targets in mines, a unified self-attention mechanism detection head (DyHead) is introduced to replace the original detection head, which greatly improves the feature expression ability of the detection head. Experimental results show that on the public dataset VOC2012, after adding DyHead to yolov8, the detection accuracy is improved by 1.5%, on the self-built dataset, after adding DyHead, the detection accuracy is improved by 2.3 %. This shows that compared with the original model, the improved algorithm after adding DyHead not only greatly improves the model detection ability, but also has good generalization performance. Compared with the mainstream algorithm, the algorithm proposed in this paper has lower model complexity and higher detection accuracy, which is suitable for target detection in complex environments in mines.

关键词： Accuracy Computational modeling Production Feature extraction Boosting Complexity theory detection algorithms Buried object detection Load modeling

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Mold detection Algorithm Based on Improved YOLOv8n

Mold Detection Algorithm Based on Improved YOLOv8n

引用

Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Zhiping Luo Yilai Zhang Chao Li Kangping Luo School of Information Engineering Jingdezhen Ceramic University Jingdezhen China Jiangxi Vocational and Technical College of Information Application Jiujiiang China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

To address the issues of low detection efficiency and accuracy, as well as the large parameters and slow speed of existing ceramic mold detection models in the ceramic product field, a lightweight ceramic mold detection algorithm based on YOLOv8n is proposed. Our improvements include: Firstly, data augmentation is performed using the GSES method on the 13KCeramics dataset. Secondly, StarNet is employed as the new backbone network to replace the original network in YOLOv8. Subsequently, the Agent attention module is added to some structures of the backbone and neck networks to enhance the model's expressive ability. Finally, Scale and Location Sensitive (SLS) loss is used to replace the original loss function. These improvements enable the model to better learn the scale and position information of ceramic molds during training, thereby further improving the accuracy of object detection. Results show that the improved algorithm achieves an increase of approximately 8.59%, 9.48%, and 0.07 in mAP@50, mAP@50-95, and F1 score, respectively, compared to the original YOLOv8n, reaching 98.7%, 98.12%, and 0.97. The FPS reaches 161 frames, an increase of approximately 13.8%, and the number of parameters is reduced by about 1.1M. These results demonstrate the significant effectiveness of the improved model.

关键词： Training Adaptation models Accuracy Shape Semantics Object detection Data augmentation Robustness Ceramics detection algorithms

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Improved Change Point detection via Bias Reduction in Parameters Estimation

Improved Change Point Detection via Bias Reduction in Parame...

引用

European Signal Processing Conference (EUSIPCO)

作者： Anthony Torre Abigael Taylor Dominique Poullin Thierry Chonavel DEMR ONERA Palaiseau France IMT Atlantique Brest France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

The recognition of unknown radar signals requires the implementation of detection algorithms in order to extract signals of interest. This paper presents an implementation of the Cumulative Sum (CuSum) algorithm for radar applications and the segmentation of unknown received signals. The study focuses on the parameters estimation of the signal distribution with an evaluation of the bias of several estimators along with the proposition of a correction factor to obtain a more robust estimator. Simulations and real data experiments validate the algorithm's capacity to retrieve both the onset and end of unknown waveforms.

关键词： Parameter estimation Magnetic resonance imaging Signal processing algorithms Radar detection Europe Data models detection algorithms Spectrogram Signal to noise ratio

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Lightweight metal parts detection algorithm based on improved YOLOv11n

Lightweight metal parts detection algorithm based on improve...

引用

Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Lingfeng Huang School of Information Engineering SHANGHAI MARITIME UNIVERSITY Shanghai China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

The progress of science and technology and the development of management concept promote the change of warehouse management to improve efficiency, reduce costs and improve service quality. As a key process, the warehouse picking mode has evolved from manual to mechanization to automation and intelligence. Although automation and intelligence improve the efficiency and accuracy of picking, many medium, small and micro enterprises are still in the stage of mechanization or manual picking. For precision parts manufacturers that cannot use automated equipment, people are optimizing picking paths and using voice technology and augmented reality (AR) technologies to improve efficiency. Augmented reality increases worker wellbeing by increasing speed and reducing error rates, but target recognition is still limited by network latency. In order to reduce the number of model parameters, narrow the model size, and ensure that the model can be successfully deployed on mobile devices with limited computing resources. This paper presents a lightweight metal parts detection algorithm based on improved YOLOv11 to improve the warehouse picking efficiency of small and medium enterprises. By constructing a metal parts data set containing real picking scenes, introducing the LSCD-Head detection head, LAMP pruning method and knowledge distillation, the model is lightweight and maintains high accuracy. The experimental results show that while maintaining the accuracy, the number of parameters and the calculation tation are significantly reduced and the speed is improved, which is better than the traditional YOLOv11n model.

关键词： Performance evaluation Solid modeling Accuracy Automation Computational modeling Metals Manuals Mobile handsets detection algorithms Augmented reality

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Lightweight Pedestrian detection Algorithm Based on Improved YoLov8s

Lightweight Pedestrian Detection Algorithm Based on Improved...

引用

Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Chenhao Zhang Weidong Li Xiaojuan Li Hebei University of Economics and Business Shijiazhuang China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

To address the high computational complexity and parameter redundancy in pedestrian detection models, this paper proposes a lightweight YOLOv8s-based algorithm optimized for resource-constrained devices. The improvements include three key components: (1) Integration of RepViT blocks into the backbone network to replace traditional bottlenecks, where the Squeeze- and-Excitation (SE) attention mechanism is selectively removed from shallow layers to reduce redundancy while retaining it in deeper layers for enhanced feature representation. (2) Replacement of standard convolutions with Group Spatial Convolution (GSConv) to minimize parameters and computational costs. (3) Introduction of the Shape-IoU loss function, which incorporates shape and scale alignment to improve bounding box accuracy for irregular pedestrian contours. Experiments on the WiderPerson dataset demonstrate that the proposed model achieves S9.5 % mAP@0.5 and 63.7% mAP@0.5:0.95, comparable to the original YOLOv8s, while reducing parameters and computational effort by 36.6% (to 7.1 MB) and 32.6% (to 19.4 GFLOPs), respectively. The model also outperforms YOLOv5n, YOLOv6n, and YOLOv8n in recall (8O.8%) and precision (S7.2 % ), achieving an optimal balance between efficiency and accuracy. These advancements enable practical deployment in intelligent transportation, surveillance, and edge-computing scenarios.

关键词： Pedestrians Accuracy Shape Computational modeling Surveillance Image edge detection Redundancy Transportation detection algorithms Standards

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A LiDAR Loop Closure detection Algorithm Integrating Multiple Information of Point Cloud

A LiDAR Loop Closure Detection Algorithm Integrating Multipl...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Chuan Zhang Qiang Hou Zhengpeng Lu Xuefen Zhu Bonan Jin Tao Zhang School of Instrument Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

In SLAM (Simultaneous Localization and Mapping) systems, the odometry in the front-end performs inter-frame matching in unstructured environments. However, due to the accumulation of errors, the accuracy of localization and mapping deteriorates. Without external observations, the loop closure optimization strategy triggered by the loop closure detection algorithm is a key method for reducing cumulative errors. Therefore, a LiDAR SLAM loop closure detection algorithm integrating multiple information of point cloud is designed. The algorithm focuses on encoding the height and density information of point cloud. Firstly, a kd-tree-based fast screening of loop closure frames is performed. Then, a refined loop closure frame selection based on similarity calculation is conducted, followed by a geometric consistency verification based on the NICP method. Experimental results on an online unstructured dataset demonstrate that the root mean square error (RMSE) of the absolute trajectory error after optimization by the proposed algorithm is reduced to 0.87 meters, showing a performance improvement of approximately 36%, with point cloud mapping results being more accurate and detailed. Moreover, the precision-recall (PR) curve of the proposed algorithm covers the largest area compared to other loop closure detection algorithms such as Scan Context and Iris.

关键词： Point cloud compression Accuracy Simultaneous localization and mapping Laser radar Search problems Encoding Trajectory Odometry detection algorithms Optimization

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A Real-time detection Algorithm Based On Improved Nanodet For Shaft Parts

A Real-time Detection Algorithm Based On Improved Nanodet Fo...

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International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Yuhan Wang Yifei Wu School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331540241

ISBN: (纸本)9798331540258

Aiming at the problems of excessive parameter amount, slow speed and low accuracy of traditional mechanical parts target detection model, a real-time shaft parts detection algorithm based on improved Nanodet is proposed. Firstly, we introduce two attention mechanism modules with no or little parameter into the original backbone network, which improve the detection effect of small targets while hardly increasing the parameters of the model. Secondly, we adopt GELU instead of the original activation function to improve the convergence speed and shorten the detection time. Finally, we adopt Soft-NMS to reduce the probability of missed and false detection in the case of target aggregation. Through experiments on the simulated shaft part dataset, mean average precision of the improved Nanodet reaches 84%, which is 12% higher than the original model, and the detection speed reaches 91 frames per second, which is 21% higher. Compared with other mainstream lightweight detection models, it also behaves better in various aspects such as speed, accuracy and model volume. The experiment results show that the improved model blances high efficiency, high accuracy and lightweight, which can accurately and quickly identify shaft part categories. Therefore, it is easy to be used on small mobile devices and has good deployment prospects.

关键词： Shafts Deep learning Solid modeling Accuracy Attention mechanisms Human-machine systems Object detection Real-time systems Mobile handsets detection algorithms

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