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检索条件"主题词=Detection Algorithms"

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A lightweight small-object detection algorithm for remote sensing images based on 4SDC-YOLOv8

A lightweight small-object detection algorithm for remote se...

引用

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

作者： Hanqing Ding Lichao Mo Shengfa Yue Jiaxue Li School of Electronics and Information Zhengzhou University of Light Industry Zhengzhou China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

In the field of object detection, remote sensing image detection has become a critical research focus. However, existing small-object detection algorithms for remote sensing images often fail to strike an effective balance between detection accuracy and computational complexity, thereby limiting their application on mobile devices. To address this issue, this paper proposes a lightweight small-object detection algorithm for remote sensing images, named 4SDC-YOLOv8, built upon YOLOv8. Firstly, we incorporate the SimSPPF spatial pyramid pooling structure, using pooling kernels of different sizes across multiple scales to improve the detector’s performance. Secondly, we design a lightweight neck network that leverages the dynamic upsampling operator DySample and the partial convolution Partial Conv to introduce the new CSPPC architecture, reducing redundant computations and enhancing both real-time performance and model compactness. Finally, we add a dedicated small-object detection layer, enabling the feature map to capture rich semantic and spatial information, with four detection heads supporting multi-scale small-object detection and improving detection capability for small targets. Experimental results on the DIOR dataset show that, compared with YOLOv8, 4SDC-YOLOv8 significantly reduces model size and computational load by 24.6% and 24%, respectively, achieves an accuracy of up to 90%, and maintains a respectable real-time performance at 145 FPS. Confirm that 4SDC-YOLOv8 effectively balances detection accuracy and computational complexity, thereby offering a promising reference for implementing small-object remote sensing detection on mobile devices.

关键词： Performance evaluation YOLO Accuracy Computational modeling Semantics Real-time systems Mobile handsets detection algorithms Computational complexity Remote sensing

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Improved YOLOv8 Factory Safety Wearable detection Algorithm

Improved YOLOv8 Factory Safety Wearable Detection Algorithm

引用

Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Wenhao Wang Buying Fu Yanlun Wu Zhenyang Yu Chenjin Sun Xin Chen Faculty of Computer Engineering Huaiyin Institue of Technology Huaian China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

In industrial scenarios, the YOLOv8 algorithm is difficult to extract key features due to the complexity of background information in images such as helmets, safety vests, and gloves, and is prone to problems such as missed detection and false detection. The C2f_MSCA structure is constructed to replace the C2f module in the Backbone network of YOLOv8 to improve the key feature extraction ability of the Backbone network. The visual information contained in the small target objects in the dataset is very limited, it is difficult to capture its features, and it is easy to be covered by complex backgrounds. A GS_Head detection branch is added to the Head part to enhance the learning ability of small targets. The receptive field attention convolution is used in the neck, which reduces background interference by adjusting the response of the feature map in the dataset and ignoring the region unrelated to the target. The experimental results show that the proposed algorithm improves the mean average precision (mAP) by 1.2 compared with the original YOLOv8 model, which effectively improves the performance of the model.

关键词： Visualization Convolution Object detection Feature extraction Production facilities Robustness Safety Neck detection algorithms Kernel

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UAVLite-YOLOv8: UAV Small Target Lightweight detection Algorithm

UAVLite-YOLOv8: UAV Small Target Lightweight Detection Algor...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Huan Liu Yusen Nie Xiaodong Cheng School of Electronic Information Engineering Inner Mongolia University Hohhot China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Aiming at the problems of UAVs in target detection, such as small target size, dense target, difficulty in feature extraction, and the excessive amount of parameters and high computation when the YOLO algorithm is applied to actual deployment, a UAV small target lightweight detection algorithm, UAVLite-YOLOv8, which is based on the improvement of YOLOv8 algorithm, is proposed. Dilation-wise Reparam Residual (DWRR) is firstly used to improve the C2f layer in the backbone, which improves the accuracy of the model while reducing the resource consumption. Global and local feature fusion (GLFF) is then used in the neck to improve the accuracy of the algorithm without significantly affecting the number of network parameters and computational effort. Finally, the detail fusion shared convolution lightweight detection (DFSCLD) head is proposed to replace the original algorithm detection head, which reduces the computational burden while maintaining the detection performance and makes the algorithm more suitable for real-time application scenarios. By conducting comparative experiments of different algorithms on the VisDrone2019 dataset, the results show that the UAVLite- YOLOv8 algorithm significantly outperforms other target detection algorithms. Compared with YOLOv8, the mAP@0.5 of UAVLite-YOLOv8 grows from 31.4% to 32.2%, the amount of parameters is reduced by 25.6%, and the amount of computation is reduced by 17.3%. In this way, it proves that the algorithm can better meet the needs of UAV small target lightweight detection.

关键词： YOLO Technological innovation Accuracy Head Quantization (signal) Object detection Autonomous aerial vehicles Real-time systems Reliability detection algorithms

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A Step-type Obstacle detection Method based on 3D Point Cloud Map

A Step-type Obstacle Detection Method based on 3D Point Clou...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Fanghong You Pengcheng Wang Rui Zhou Yihan Zhang Jian Zhang Tao Zhao College of Electrical Engineering Sichuan University Chengdu China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Addressing the challenge of precise detection of traversable obstacles resembling steps in 3D point cloud map generated by LiDAR, this paper proposes a detection algorithm based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and geometric constraints. The proposed method achieves accurate detection of step-type traversable obstacles in 3D point cloud map. Firstly, the 3D point cloud map is filtered to remove outliers and extract regions of interest. Then, the Random Sample Consensus (RANSAC) plane fitting algorithm is employed to detect and remove ground points for eliminating clustering interference. Subsequently, the DBSCAN algorithm is applied for density clustering of the point cloud map without ground points. Finally, geometric constraints resembling step-type traversable obstacles are applied to the clustering results to select the optimal detection targets. Experimental results demonstrate that the proposed detection algorithm can accurately detect various step-type traversable obstacles in indoor 3D point cloud maps, achieving desirable outcomes.

关键词： Point cloud compression Three-dimensional displays Accuracy Laser radar Noise Neural networks Clustering algorithms Robustness Labeling detection algorithms

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Pedestrian detection Algorithm Based on Multi-Scale Fusion and Dual-Backbone

Pedestrian Detection Algorithm Based on Multi-Scale Fusion a...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Deshuai Bao Wenzhong Zhu School of Computer Science & Engineering Sichuan University of Science & Engineering Yibin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Addressing the limitations of visible light singlemodal target detection algorithms, which are affected by external factors like illumination, and infrared single-modal algorithms, which lack complete target information, we propose a pedestrian detection algorithm based on dual-backbone and multi-modal multi-scale fusion, building upon *** network employs a dual-backbone structure derived from the YOLOv8n lightweight network. An SMMF(Scale-Multiple Multimodal Feature Fusion) module is designed to fuse features from both modalities at multiple scales. The GhostConv module is introduced to enrich features and reduce computational cost. Additionally, the SBAp2 is integrated into the network neck, enhancing the fusion of deep and shallow features and leveraging both deep semantic information and shallow image location information. The ShapeIoU is adopted as the loss function, replacing the original bounding box regression loss, to incorporate the shape and scale of the bounding box into the loss calculation, thereby improving target detection accuracy. Experimental results indicate that the improved algorithm achieves an AP0.5 of 78.5% and an FPS of 156.3. Compared to the original visible light single-modal and infrared single-modal YOLOv8n algorithms, the missed detection rates were reduced by 30.1% and 13.4%, respectively, while the AP0.5 is increased by 21.2% and 8.2%, demonstrating its superiority over the single-modal YOLOv8n detection algorithm.

关键词： Pedestrians Accuracy Shape Semantics Object detection Reconnaissance Real-time systems Neck detection algorithms Standards

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detection of False Data Injection Attacks in New Energy Systems Based on Supervised Learning

Detection of False Data Injection Attacks in New Energy Syst...

引用

Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Tong Li Lei Wang Jian Chen Yang Liu Hongwei Zhao Hongbi Geng State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang China State Grid Liaoning Electric Power Research Institute Shenyang China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

In recent years, with the rapid development of China’s new energy industry, and to the monitoring and data acquisition (SCADA) system brings a huge network security risks. False Data Injection Attack (FDIA) is a serious threat to the secure and stable operation of smart grids. In this paper, an FDIA detection algorithm that integrates unsupervised and supervised learning is proposed to address the problems of scarce labelled data and extreme imbalance between normal and attack samples in FDIA detection. Compared with other FDIA detection algorithms, the proposed method enhances the stability of the FDIA detection model in the case of scarce labelled samples and unbalanced data, improves the recognition accuracy of FDIA and reduces the false alarm rate.

关键词： Accuracy Supervised learning SCADA systems Power system stability Network security Stability analysis Smart grids detection algorithms Thermal stability Monitoring

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Research on the detection Algorithm for Abnormal Crowd Behaviors Based on an Enhanced SlowFast Model

Research on the Detection Algorithm for Abnormal Crowd Behav...

引用

International Conference on Computers, Communications, and Systems (ICCCS)

作者： Yueping Peng Hexiang Hao Tongtong Zhou Baixuan Han Wenji Yin Information Engineering Collage Engineering University of PAP Xian China Engineering University of PAP Xian China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Aiming at the issues of inaccurate detection of behavioral classification in crowd abnormal behaviors detection algorithms and large model computation capacity, a crowd abnormal behaviors detection algorithm based on the improved SlowFast model is proposed. By changing the convolutional structure of the network to a Depthwise-separable convolution, adding a hybrid domain attention mechanism, and inserting Dropout to inhibit the overfitting phenomenon, we can enhance the network's precision of detection and classification of behaviors while ensuring the computational capacity. Experiments show that the method in this paper can detect and identify five types of behaviors in public places: normal, scattered, speeding up in the same direction, sudden gathering, and group fighting. Compared with the original SlowFast model and other algorithms, the precision is improved, and the number of detected frames per second can be up to 40.5 fps, which meets the real-time requirements, providing a new idea for the determination and classification of abnormal behaviors of crowds.

关键词： Attention mechanisms Convolution Communication systems Computational modeling Real-time systems Classification algorithms detection algorithms

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R-Wave detection Algorithm Based on Central Difference and Adaptive Multi-Threshold

R-Wave Detection Algorithm Based on Central Difference and A...

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Computer Information Science and Application Technology (CISAT), International Conference on

作者： Ke Xu Huibin Wang Sikai Guo Wenping Chen College of Information Science and Engineering Hohai University HHU Nanjing China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

As a non-invasive method for diagnosing heart diseases, electrocardiography (ECG) relies on R-wave detection as a foundational step for other ECG waveform analysis. To further meet the demands for accuracy and real-time detection of R-waves in automatic ECG analysis, this paper proposes a low-complexity, fast, and reliable real-time R-wave detection algorithm. An R-wave detection method based on central difference and adaptive multi-threshold is designed. First, this paper proposes the use of central difference for R-wave enhancement, effectively improving the detection capability for low amplitude and low slope R-waves. Secondly, an adaptive multi-threshold strategy is proposed, which adaptively adjusts the current detection threshold based on the detected R-wave amplitude and RR interval, making it suitable for R-wave detection in cases of sudden changes in RR interval and R-wave amplitude. The proposed algorithm’s performance was evaluated using ECG signals from the MIT-BIH database, achieving an overall sensitivity, positive predictive value, and detection error rate of 99.73%, 99.82%, and 0.46%, respectively. The processing time for a 30-minute ECG recording is approximately 0.52 seconds. The results indicate that the algorithm is accurate, efficient, and suitable for real-time automatic ECG analysis.

关键词： Accuracy Sensitivity Machine learning algorithms Electrocardiography Prediction algorithms Approximation algorithms Real-time systems Recording Reliability detection algorithms

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Automatic Pain detection Algorithm for Patients with Cancer Pain Using Wristwatch Wearable Devices

Automatic Pain Detection Algorithm for Patients with Cancer ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Hideyuki Hirayama Shiori Yoshida Konosuke Sasaki Emi Yuda Kento Masukawa Mamiko Sato Tomoo Ikari Akira Inoue Yoshihide Kawasaki Mitsunori Miyashita Department of Palliative Nursing Tohoku University Graduate School of Medicine Sendai Japan Department of Oncology Nursing Tohoku University Graduate School of Medicine Sendai Japan Tohoku University Graduate School of Information Sciences Sendai Japan Department of Palliative Medicine Tohoku University Graduate School of Medicine Sendai Japan Department of Urology Tohoku University Graduate School of Medicine Sendai Japan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Pain assessment becomes challenging for patients unable to self-report, given the subjective nature of pain. This study introduces an automatic pain detection model utilizing biological signals from wristwatch wearables and time series data from patients with cancer experiencing pain. Biological signals and pain data were obtained from 10 patients with cancer pain for 7 days during their hospitalization. A total of 73,154 minutes of data and 407 pain reports were obtained. We developed automatic classifiers to detect moderate or severe pain and pain above the personalized pain goal by several machine learning algorithms using per-patient and mixed data sets. The best-performing algorithm achieved an F1 score of 0.87, with enhanced performance using the personalized pain goal as the cutoff. While the generalized model requires improvement, the study demonstrates the feasibility of automatic pain detection using extended real-world patient data.

关键词： Performance evaluation Machine learning algorithms Pain Biological system modeling Time series analysis Data models Object recognition Wearable devices detection algorithms Cancer

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STOE-YOLOv5s: A Hard Hat Wearing detection Algorithm Based on Improved YOLOv5s

STOE-YOLOv5s: A Hard Hat Wearing Detection Algorithm Based o...

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Robotics and Computer Vision (ICRCV), International Conference on

作者： Tao Hu Wei Huang Jianghao Wei Qiang Liu Jiahuan Huang Jie Yu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

To address the existing issues of missed detection and false detection of hard hats by mainstream detection models in complex environments, an improved hard hat detection algorithm, STOE-YOLOv5s based on YOLOv5s, has been proposed. Firstly, enhance the detection header structure, add multi-scale target detection layers, and improve the detection capability for small targets; Secondly, a BottleNeck Transformer (BoT3) module based on self-attention is added to the backbone network to enhance the model's ability to extract global features; Then, to solve the problem of positive and negative sample matching, a Simplified Optimal Transport Assignment (SimOT A) strategy is used to obtain the optimal matching result in the global range; Finally, an Efficient Multi-Scale Attention (EMA) mechanism is fused into the C3 module of the network neck to enhance the model's ability to capture features-rich areas. Experimental results on the hard hat dataset show that the mAP of the improved algorithm reaches 96.0%, and the recall rate reaches 91.8%. Compared with the original YOLOv5s algorithm, it has increased by 1.7 and 2.9 percentage points respectively, proving the effectiveness and advancement of the improved algorithm.

关键词： YOLO Head Accuracy Computational modeling Feature extraction Transformers Safety Resource management detection algorithms Optimal matching

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