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4th IEEE International Conference on Data Science and Computer Application, ICDSCA 2024

作者： Gao, Pengyuan Liu, Lijie Song, Xinzhe Wang, Yubo Wu, Siting Chi, Ruyue Heilongjiang Bayi Agricultural University Daqing City China

ISBN: (纸本)9798350368239

In order to overcome the low efficiency of digital image processing, this paper introduces DNN to detect wall cracks. By constructing the training data set of deep learning model, the method of test image acquisition and preprocessing is studied, and the test image library of crack detection model is established. The crack open data set is used to improve the crack training. The deep learning target detection algorithm was studied, and the wall crack recognition and positioning model based on yolov8 algorithm was constructed. The width, depth parameters and boundary frame loss function of yolov8 algorithm were optimized and adjusted by using the crack recognition data set. It effectively reduces the risk and cost of detection work, and has a strong engineering application prospect and social value. ©2024 IEEE.

关键词： Deep learning Recognition algorithm Wall cracks yolov8 algorithm

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An improved lightweight tiny-person detection network based on yolov8: IYFVMNet

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FRONTIERS IN PHYSICS 2025年 13卷

作者： Yang, Fan Pan, Lihu Cui, Hongyan Zhang, Linliang Taiyuan Univ Sci & Technol Coll Comp Sci & Technol Taiyuan Shanxi Peoples R China Shanxi Intelligent Transportat Inst Co Ltd Taiyuan Shanxi Peoples R China

Introduction In the aviation field, drone search and rescue is a highly urgent task involving small target detection. In such a resource-constrained scenario, there are challenges of low accuracy and high computational *** This paper proposes IYFVMNet, an improved lightweight detection network based on yolov8. The key challenges include feature extraction for small objects and the trade-off between detection accuracy and speed. To address these, four major innovations are introduced: (1) Fasternet is used to improve the bottleneck structure in the cross-stage feature fusion backbone network. This approach fully utilizes all feature map information while minimizing the computational and memory requirements. (2) the neck network structure is optimized using the Vovnet Gsconv Cross Stage Partial module. This operation also reduces the computational cost by decreasing the amount of required feature map channels, while maintaining the effectiveness of the feature representation. (3) he Minimum Point Distance Intersection over Union loss function is employed to optimize bounding box detection during model training. (4) to construct the overall network structure, the Layer-wise Adaptive Momentum Pruning algorithm is used for *** Experiments on the TinyPerson dataset demonstrate that IYFVMNet achieves a 46.3% precision, 30% recall, 29.3% mAP50, and 11.8% *** The model exhibits higher performance in terms of accuracy and efficiency when compared to other benchmark models, which demonstrates the effectiveness of the improved algorithm (e.g., YOLO-SGF, Guo-Net, TRC-YOLO) in small-object detection and provides a reference for future research.

关键词： small object detection yolov8 algorithm FasterNet vovnet gsconv LAMP

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Safety Helmet Detection Based on Improved yolov8

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IEEE ACCESS 2024年 12卷 28260-28272页

作者： Lin, Bingyan Fujian Polytech Informat Technol Fuzhou 350003 Peoples R China

Wearing safety helmets can effectively reduce the risk of head injuries for construction workers in high-altitude falls. In order to address the low detection accuracy of existing safety helmet detection algorithms for small targets and complex environments in various scenes, this study proposes an improved safety helmet detection algorithm based on yolov8, named yolov8n-SLIM-CA. For data augmentation, the mosaic data augmentation method is employed, which generates many tiny targets. In the backbone network, a coordinate attention (CA) mechanism is added to enhance the focus on safety helmet regions in complex backgrounds, suppress irrelevant feature interference, and improve detection accuracy. In the neck network, a slim-neck structure fuses features of different sizes extracted by the backbone network, reducing model complexity while maintaining accuracy. In the detection layer, a small target detection layer is added to enhance the algorithm's learning ability for crowded small targets. Experimental results indicate that, through these algorithm improvements, the detection performance of the algorithm has been enhanced not only in general scenarios of real-world applicability but also in complex backgrounds and for small targets at long distances. Compared to the yolov8n algorithm, yolov8n-SLIM-CA shows improvements of 1.462%, 2.969%, 2.151%, and 3.549% in precision, recall, mAP50, and mAP50-95 metrics, respectively. Additionally, yolov8n-SLIM-CA reduces the model parameters by 6.98% and the computational load by 9.76%. It is capable of real-time and accurate detection of safety helmet wear. Comparison with other mainstream object detection algorithms validates the effectiveness and superiority of this method.

关键词： Safety helmet detection yolov8 algorithm yolov8n-SLIM-CA coordinate attention mechanism slim-neck

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Advances in ASL Detection: a yolov8-based framework

Advances in ASL Detection: a YOLOv8-based framework

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International Conference on Machine Intelligence and Smart Innovation (ICMISI)

作者： Emam, Mohamed Hesham Ghaly, Abdelrhamn Mostafa, Ahmed Ayman, Ali Adel, Aya Tawfik, Mohamed Badawy, Wael Egyptian Russian Univ Sch Artificial Intelligence Cairo Egypt Egyptian Russian Univ Sch Artificial Intelligence Dept Artificial Intelligence Cairo Egypt Egyptian Russian Univ Dept Data Sci Sch Artificial Intelligence Cairo Egypt

ISBN: (纸本)9798350365740;9798350365757

We present in this paper a yolov8-based framework to accurate detection of American Sign Language "ASL". ASL is widely used in bridging the "communication" among hearing communities. The proposed framework provides a real-time detection of gesture in a complex environment. The proposed framework has been development, implemented, and tested using 6033 images with size of 416x416. It contains 36 classes, 26 classes for Letters ('A' - 'Z'), and 10 classes for Numbers ('0' - '9'). The training dataset have been augmented to better increase the accuracy of detection. The simulation results shows that the efficiency of detecting in a high frame rate.

关键词： American Sign Language "ASL" recognition yolov8 algorithm real-time gesture detection assistive technology sign-to-text translation.

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Advancing architectural heritage: precision decoding of East Asian timber structures from Tang dynasty to traditional Japan

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HERITAGE SCIENCE 2024年第1期12卷 1-19页

作者： Gao, Chan Zhao, Genfeng Gao, Sen Du, Shuxin Kim, Eunyoung Shen, Tao Japan Adv Inst Sci & Technol Grad Sch Adv Sci & Technol Nomi 9231292 Japan Hangzhou R&F Real Estate Dev Co Ltd Hangzhou 310000 Zhe Jiang Peoples R China Baoding Shengyang Real Estate Dev Co Ltd Baoding 071000 Hebei Peoples R China Huzhou Univ Huzhou 313000 Zhe Jiang Peoples R China Huzhou Univ Sch Engn Huzhou Key Lab Intelligent Sensing & Optimal Contr Huzhou 313000 Peoples R China Tongji Univ Coll Design & Innovat Shanghai 200092 Peoples R China

The convergence of cultural and aesthetic elements in timber structures from China's Tang Dynasty (618-907 AD) and traditional Japanese architecture provides a rich tapestry of architectural evolution and cross-cultural exchanges. Addressing the challenge of distinguishing and understanding the intricate styles of these structures is significant for both historical comprehension and preservation efforts. This research introduces an innovative approach by integrating the Multi-Head Attention (MHA) mechanism into the yolov8 model, enhancing the detection of architectural features with improved precision and recall. Our novel yolov8-MHA model not only demonstrates a notable improvement in recognizing intricate architectural details but also significantly advances the state of the art in object detection within complex settings. Quantitative results underscore the model's effectiveness, achieving a precision of 95.6%, a recall of 85.6%, and a mean Average Precision (mAP@50) of 94% across various Intersection over Union (IoU) thresholds. These metrics highlight the model's superior capability to accurately identify and classify architectural elements, especially within environments rich with nuanced details, utilizing the enhanced yolov8-MHA algorithm. The application of our model extends beyond mere architectural analysis;it offers new insights into the intricate interplay of cultural identity and adaptability inherent in East Asian architectural heritage. The study establishes a solid foundation for the meticulous classification and analysis of architectural styles in timber structures within an expansive cultural and historical context, thereby enriching our understanding and preservation of these traditions.

关键词： Cultural heritage conservation Historic timber structures Tang dynasty architecture Japanese traditional architecture Object detection yolov8 algorithm

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Soft X-ray image recognition and classification of maize seed cracks based on image enhancement and optimized yolov8 model

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 216卷

作者： Chen, Siyu Li, Yixuan Zhang, Yidong Yang, Yifan Zhang, Xiangxue Beijing Forestry Univ Coll Sci Beijing 100083 Peoples R China Beijing Forestry Univ Coll Biol Sci & Biotechnol Beijing 100083 Peoples R China

The current investigation on image recognition and internal crack detection of maize seeds primarily relies on visible light imaging. However, due to the low transmissivity of plant cells, even with image enhancement measures, the clarity of internal cracks in the images and the subsequent feature extraction process can be a trade-off. Soft X-rays possess exceptional penetration capability and offer better safety and convenience compared to hard X-rays, making them highly suitable for visualizing internal structures within plant tissues like maize seeds. In this paper, a non-invasive Imaging Technique for Image Enhancement is proposed, combining wavelet thresholding denoising, image standardization, bilateral filtering, and laplacian sharpening. This method is based on soft X-rays and successfully achieves image recognition of cracks present inside Zhengdan 958 maize seeds using an optimized yolov8 model. It effectively addresses challenges related to the limited light transmission of maize seeds, difficulty in crack localization, and algorithm generalization issues. The optimized yolov8 model demonstrates an average precision (AP) value that is 3.1% higher than that of the original model. Furthermore, by applying image enhancement, the AP value increases by 1.8%. The proposed method exhibits an average recognition accuracy of 99.66% for intact or broken seeds, an average precision of 99.87%, an average recognition recall of 99.48%, and an average single-frame image detection time of 7.49 ms in the single seed detection experiment.

关键词： Maize seed Soft X -ray Image enhancement yolov8 algorithm Internal crack detection

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Improved yolov8 for Gas-Flame State Recognition under Low-Pressure Conditions

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SENSORS 2024年第19期24卷 6383页

作者： Sai, Qingyi Zhao, Jin Bi, Degui Qin, Bo Meng, Lingshu Univ Shanghai Sci & Technol Coll Energy & Power Engn Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Coll Environm & Architecture Shanghai 200093 Peoples R China

This paper introduces a lightweight flame detection algorithm, enhancing the accuracy and speed of gas-flame state recognition in low-pressure environments using an improved yolov8n model. This method effectively resolves the aforementioned problems. Firstly, GhostNet is integrated into the backbone to form the GhostConv module, reducing the model's computational parameters. Secondly, the C2f module is improved by integrating RepGhost, forming the C2f_RepGhost module, which performs deep convolution, extends feature dimensions, and simplifies the inference structure. Additionally, the CBAM attention mechanism is added to enhance the model's ability to capture fine-grained features of flames in both channel and spatial dimensions. The replacement of CIoU with WIoU improves the sensitivity and accuracy of the model's regression loss. Experimental results on a simulated dataset of the theoretical testbed indicate that compared to the original model, the proposed improvements achieve good performance in low-pressure flame state detection. The model's parameter count is reduced by 12.64%, the total floating-point operations are reduced by 12.2%, and the detection accuracy is improved by 21.2%. Although the detection frame rate slightly decreases, it still meets real-time detection requirements. The experimental results demonstrate that the feasibility and effectiveness of the proposed algorithm have been significantly improved.

关键词： low-pressure environment flame detection yolov8 algorithm attention mechanism

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Research on Infrared Dim Target Detection Based on Improved yolov8

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REMOTE SENSING 2024年第16期16卷 2878页

作者： Liu, Yangfan Li, Ning Cao, Lihua Zhang, Yunfeng Ni, Xu Han, Xiyu Dai, Deen Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China 63615 Unit Chinese Peoples Liberat Army Xinjiang 841000 Peoples R China

Addressing the formidable challenges in spatial infrared dim target detection, this paper introduces an advanced detection approach based on the refinement of the yolov8 algorithm. In contrast to the conventional yolov8, our method achieves remarkable improvements in detection accuracy through several novel strategies. Notably, by incorporating a deformable convolutional module into the yolov8 backbone network, our method effectively captures more intricate image features, laying a solid foundation for subsequent feature fusion and detection head predictions. Furthermore, a dedicated small target detection layer, built upon the original model, significantly enhances the model's capability in recognizing infrared small targets, thereby boosting overall detection performance. Additionally, we utilize the WIoU-v3 as the localization regression loss function, effectively reducing sensitivity to positional errors and leveraging the advantages of multi-attention mechanisms. To enrich the quantity and quality of the spatial infrared dim target dataset, we employ image enhancement techniques to augment the original dataset. Extensive experiments demonstrate the exceptional performance of our method. Specifically, our approach achieves a precision of 95.6%, a recall rate of 94.7%, and a mean average precision (mAP) exceeding 97.4%, representing substantial improvements over the traditional yolov8 algorithm. Moreover, our detection speed reaches 59 frames/s, satisfying the requirements for real-time detection. This achievement not only validates the efficacy and superiority of our algorithm in spatial infrared dim target detection, but also offers novel insights and methodologies for research and applications in related fields, holding immense potential for future applications.

关键词： spatial infrared dim target detection yolov8 algorithm deformable convolution image enhancement real-time detection

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Automatic weapon detection using Deep Learning

Automatic weapon detection using Deep Learning

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Akshaya, Poloju Gurusai, P. Reddy, Pamulaparthy Bharath Panuganti, Pratheek Subhahan, Abdus Computer Science and Engineering B V Raju Institute of Technology Narsapur India

ISBN: (纸本)9798350305708

Safety and security is important in today's world scenario. For a country to be strong in terms of economy it must have secure and safe environment. CCTV surveillance is majorly used to monitor activities such as robbers and threats in public places, but these cameras need constant human observation. The fast and accurate automatic weapon detection system is useful to avoid these types of risks in public places such as schools, hospitals, museum and traffic etc. In many places the crimes are caused by pistols, guns and knives are very more, mainly in the countries and cities where there is no gun control laws. This work, focuses on automatic weapon detection in CCTV footage by making use of deep learning algorithms. In our work we are going to use yolov8 model to detect weapons as it is better in terms of accuracy and speed when compared to other models. It uses larger data set which consists of various images of weapons. Data set includes pictures from youtube robbery videos, CCTV footages, GitHub repositories and Internet movies firearms data base. Sliding window technique is used to extract features from objects and classify weapons. yolov8 has shown improvement in detection with greater speed and high accuracy. © 2023 IEEE.

关键词： Alert generation CCTV Surveillance Deep Learning Object detection Sliding window technique Weapon Detection yolov8 algorithm

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MST-YOLO: Small Object Detection Model for Autonomous Driving

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SENSORS 2024年第22期24卷 7347页

作者： Li, Mingjing Liu, Xinyang Chen, Shuang Yang, Le Du, Qingyu Han, Ziqing Wang, Junshuai Changchun Univ Coll Elect Informat Engn Changchun 130022 Peoples R China Shangqiu Inst Technol Sch Informat & Elect Engn Shangqiu 476000 Peoples R China

Autonomous vehicles operating in public transportation spaces must rapidly and accurately detect all potential hazards in their surroundings to execute appropriate actions such as yielding, lane changing, and overtaking. This capability is a prerequisite for achieving advanced autonomous driving. In autonomous driving scenarios, distant objects are often small, which increases the risk of detection failures. To address this challenge, the MST-yolov8 model, which incorporates the C2f-MLCA structure and the ST-P2Neck structure to enhance the model's ability to detect small objects, is proposed. This paper introduces mixed local channel attention (MLCA) into the C2f structure, enabling the model to pay more attention to the region of small objects. A P2 detection layer is added to the neck part of the yolov8 model, and scale sequence feature fusion (SSFF) and triple feature encoding (TFE) modules are introduced to assist the model in better localizing small objects. Compared with the original yolov8 model, MST-yolov8 demonstrates a 3.43% improvement in precision (P), an 8.15% improvement in recall (R), an 8.42% increase in mAP_0.5, a reduction in missed detection rate by 18.47%, a 70.97% improvement in small object detection AP, and a 68.92% improvement in AR.

关键词： autonomous driving yolov8 algorithm small object detection

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