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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Li, Qiangqiang Yu, Kun Wang, Haoran Guan, Qianjun Gao, Shihong Jiang, Jiamin Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huai'an Jiangsu China

ISBN: (纸本)9798350306293

Wearing a safety helmet at the work site of a chemical plant can effectively prevent safety accidents caused by head injuries, so it is very important to detect whether employees wear safety helmets. In order to solve problems such as serious information loss, large number of parameters, and weak detection ability of small targets in complex scenes in the sampling process of helmet detection algorithm. In this paper, an improved lightweight safety helmet detection algorithm LP- YOLOv5m (Lightweight And High Precision YOLOv5m) based on YOLOv5m is designed. LP-YOLOv5m incorporates the Wise-IoU (Wiou) loss function to enhance model generalization. Additionally, we add a small object detection layer to improve the feature extraction ability for small objects. Furthermore, upsampling was performed using CARAFE. Finally, a lightweight module named C3-F was proposed, which replaced the Bottleneck in the C3 module with FasterNet Block, which could effectively reduce the network model size. Comparative laboratory findings demonstrate that our improved model achieves a mAP50 of 92.1%, surpassing YOLOv5m by 3 percentage points while reducing the model size by 23.3%. The LP-YOLOv5m model can be better applied to practical scene requirements, and can effectively solve practical problems. © 2023 IEEE.

关键词： Chemical plants

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Identity-Based Key-Preserved Compacted Representative Signature as a Teaching Case of Cryptography

Identity-Based Key-Preserved Compacted Representative Signat...

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2023 International Conference on computer Network Security and software engineering, CNSSE 2023

作者： Chen, Jianhong Faculty of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huaian223003 China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

An compacted representative signature is a string that persuade the checker to believe that, for all 1 ≤ i ≤ n, the i-th representative signer approved a piece of information. The loss of clandestine keys threatens a security system. We come up with the idea of identity-based key-preserved compacted representative signature (IBKPCRS) in which representative signing keys vary over time. The usage of IBKPCRS is to meet the key-revealing challenge. In the IBKPCRS system, the user sets his telephone number or other overt string as his public key. We give the IBKPCRS scheme as a teaching case of cryptography. © 2023 SPIE.

关键词： Cryptography

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Steel surface defect detection based on Improved YOLOX Algorithm

Steel surface defect detection based on Improved YOLOX Algor...

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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Li, Guohao Mokhtarzadeh, Amir Ali School of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huaian223003 China

As one of the very common industrial products, strip steel plays a very important role in various fields. Steel production has received extensive attention. In industrial production, scratches are unavoidable due to numerous forces beyond human control. Cracks and other flaws appear, and their presence has a direct impact on the product's quality. Therefore, it is of great significance to develop efficient and accurate strip surface defect detection. In this study, the most widely used metal material strip steel is selected as the research object, and its surface defect detection problem is studied accordingly. The activation function and loss function are improved on the basis of the YOLOX model. Resolve the problem of overexposure and content distortion of the image caused by the high reflecting characteristics of the strip, which degrades the image quality, and the embedding of the sub-attention mechanism to enhance the regional characteristics of hidden small targets. The experimental results show that the mAP of the improved model for strip surfaces defect detection is 80.7%, which is 4.1% higher than that of the original YOLOX algorithm, and the improved model can reduce the false detection and missed detection rate of defects. © Published under licence by IOP Publishing Ltd.

关键词： Strip metal

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Novel cartographer using an OAK-D smart camera for indoor robots location and navigation

Novel cartographer using an OAK-D smart camera for indoor ro...

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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Han, Yunpeng Ali Mokhtarzadeh, Amir Xiao, Shaozhang School of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huai'an223003 China

In recent years, service robots have been widely used in people's daily life, and with the development of more and more intelligence, people put forward higher requirements for autonomous positioning and navigation functions of robots. Like outdoor navigation, indoor navigation also needs the support of navigation data. Although the indoor positioning and navigation scheme based on cameras, lidars and other sensors is gradually developing, due to the complexity of the indoor structure, manual production of indoor navigation data is time-consuming and laborious, and the efficiency is relatively low. In order to solve the problem of low productivity and improve the accuracy of robot automatic navigation, we added a new type of intelligent camera, called OpenCV AI kit or OAK-D, and proposed a method to automatically build data files that can be used for indoor navigation and location services using indoor 3D point cloud data. This intelligent camera performs neural reasoning on chips that do not use GPUs. It can also use stereo drills for depth estimation, and use 4K color camera images as input to run the neural network model. Python API can be called to realize real-time detection of indoor doors, windows and other static objects. The target detection technology uses an artificial intelligence camera, and the robot can well identify and accurately mark on the indoor map. In this paper, a high-performance indoor robot navigation system is developed, and multisensor fusion technology is designed. Environmental information is collected through artificial intelligent camera (OAK-D), laser lidar, and data fusion is carried out. In the experiment part of this paper,The static fusion map module is created based on the laser sensor information and the sensor information of the depth camera, the hierarchical dynamic cost map module is created in the real-time navigation, and the global positioning of the robot is realized by combining the word bag model and the laser point cl

关键词： Cameras

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FastVGG network and its application in automatic identification of traffic signs

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International Journal of Information and Communication technology 2022年第2期20卷 192-203页

作者： Gui, Kaige Gao, Shangbing Li, Xinyuan Lu, Zhonghe College of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China Jiangsu Mobile Internet of Things Technology Engineering Laboratory Huaian223001 China

Traffic sign recognition is an important part of driver assistance systems. Because the types of traffic signs are complex and diverse, they are difficult to identify. Traditional recognition methods require manual extraction of features, which is difficult and inaccurate. This paper proposes a FastVGG network based on VGG neural network to extract the features of the target image to realise the recognition of traffic signs under different angles and illumination. In the method of this paper, the connection layer parameters and the number of network layers are moderately reduced, the merging step is increased, and the recognition speed is improved. When the parameter value is less than zero, the leaky ReLU is used to replace the activation function to solve the problem of neuron death. The experimental results of the German traffic sign recognition dataset (GTSRD) show that the algorithm can achieve accurate classification while increasing the speed. © 2022 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Traffic signs

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An Image-based Method for Tyre Conformity Detection

An Image-based Method for Tyre Conformity Detection

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Shao, Heshuai Yu, Xuan Qiu, Junlin Huaiyin Institute of Technology College of Computer and Softwaer Engineering Huaian China

ISBN: (纸本)9798350306293

This paper proposes an image-based tyre conformity inspection method for the traditional tyre conformity inspection relying on manual inspection, which is subjectively influenced by human and has low working efficiency. The method uses a parabolic point calculation method to accurately measure the arc length of the tyre and uses an elliptical LBP algorithm combined with a hash algorithm to effectively compare the tyre surface tread pattern with the template. Experiments on real-life scenarios prove that the method meets the requirements of tyre conformity inspection accuracy and is highly accurate, so as to achieve more efficient and stable quality control in the production process, providing a useful reference for intelligent tyre conformity inspection. © 2023 IEEE.

关键词： Tires

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A System for Detecting and Locating Cracks in Dam Foundations on a Mobile Underwater Platform

A System for Detecting and Locating Cracks in Dam Foundation...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Yu, Xuan Shao, Heshuai Qiu, Junlin Huaiyin Institute of Technology College of Computer and Softwaer Engineering Huaian China

ISBN: (纸本)9798350306293

In this paper, a system for detecting and locating cracks in dam foundations on underwater mobile platforms is proposed to address the technical problems of existing underwater dam foundation crack detection, such as serious image quality damage, incorrect extraction of crack information and inability to accurately locate cracks. The system uses an improved dark channel algorithm to defog the underwater images taken by the camera, combines the intelligent water droplet algorithm and the simulated annealing algorithm to extract the crack information from the defogged target images, and obtains the crack location by laser triangulation and conversion of the camera coordinate system. Experiments on real scenarios demonstrate that the method is able to reduce image quality impairment, extract valid crack information and return the location of cracks in the dam base, providing prior conditions for monitoring and repairing cracks in the dam base. © 2023 IEEE.

关键词： Location

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Research on Residual Value Prediction of New Energy Second-hand Cars based on Prophet Multivariate Time Series Model

Research on Residual Value Prediction of New Energy Second-h...

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2022 International Conference on computer, Artificial Intelligence, and Control engineering, CAICE 2022

作者： Shuhao, Sun Hong, Zhou Junjie, Ji Siye, Liu Xiangyu College of Huaiyin Institute of Technology Jiangsu Huai'an China Computer and Software Engineering College of Huaiyin Institute of Technology Jiangsu Huai'an China

ISBN: (纸本)9781510655942

Nowadays, the rapid development of new energy vehicles has also driven the new energy second-hand car market. This study aims to solve the current problems of messy residual value evaluation of new energy second-hand cars, and better predict the residual value through the loss variables. It crawled the data from the second-hand car website through crawlers and found that new energy second-hand cars have trends, periodicity, and some additional effects. Therefore, this study adopted the Prophet Multivariate Time Series model for prediction whose input is the licensing time and kilometers, and output is the predicted price. Through trend comparison and accuracy evaluation, the model showed better prediction performance whose prediction accuracy of the residual value of new energy second-hand vehicles can reach 96%. © 2022 SPIE.

关键词： Value engineering

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Application of data twinning based on deep time series model in smart city traffic flow prediction

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Gao, Li Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an223003 China

This paper introduces an intelligent traffic flow prediction system that combines data twinning and deep learning, aiming to improve the prediction accuracy and model adaptability by integrating grey prediction model (GM(1,1)), long-short-term memory network (LSTM) and particle swarm optimization (PSO). The system construction starts from physical layer data acquisition, deals with missing data through smoothing and interpolation, and applies the GM(1,1) model to construct a digital twin layer for preliminary prediction. The information fusion mechanism combines real-time data and forecast data to optimize the model inputs, and the PSO algorithm is used to optimize the model parameters. The LSTM model is used as a deep time-series model, and combined with the output of the data twin prediction to make a comprehensive prediction, and at the same time, feature engineering is utilized to enhance the model performance. The system implements dynamic feedback adjustment to optimize the model in real time according to the prediction error. Empirical analysis shows that the LSTM model with integrated data twin (LSTM + DT) outperforms the traditional model in prediction accuracy, stability, and generalization ability, especially in peak hours, holidays, and emergency response, and provides a reliable solution for intelligent traffic management despite the long training time. © The Author(s) 2025.

关键词： Deep learning

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FDM-RTDETR: A Multi-scale Small Target Detection Algorithm

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IEEE Access 2025年 13卷 88747-88761页

作者： Wang, Hongya Yu, Yongtao Tang, Zhaoxia Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian223003 China

To address challenges in UAV object detection including complex backgrounds, severe occlusion, dense small objects, and varying lighting conditions, we propose FDM-DETR, a novel detection algorithm specifically designed for small objects in UAV imagery. This method effectively captures global image information by fusing multi-scale spatial features and performing feature extraction in the frequency domain within the backbone network. We design a Dynamic Feature Interaction (DIFI) module with position-based biases in the encoder, enhancing the model’s perception of local features for small objects. In the neck network, we introduce a Multi-Scale Feature Enhancement Pyramid (MSFEP) module to improve feature extraction capabilities for small object detection. Compared to RT-DETR, our improved model achieves performance gains of 2.5% and 2.7% in AP on the Vis-Drone2019 validation and test sets, respectively. While maintaining low computational complexity and parameter count, the method demonstrates significant improvements in detection performance. FDM-DETR exhibits robust practicality and reliability in UAV-based small object detection tasks. © 2013 IEEE.

关键词： Target drones

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