2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)最新文献

Infant Facial Expression Recognition Based on Parameter-free Attention Module 基于无参数注意模块的婴儿面部表情识别

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930204

Congcong Li, Xi Li, Tian Li

引用次数: 0

Agricultural light-trapped pest detection methods under unbalanced data 不平衡数据下的农业捕光害虫检测方法

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930207

Jiaqi Wang, Wei Huang, Qi Zhang

{"title":"Agricultural light-trapped pest detection methods under unbalanced data","authors":"Jiaqi Wang, Wei Huang, Qi Zhang","doi":"10.1109/AICIT55386.2022.9930207","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930207","url":null,"abstract":"This paper designs an improved YOLOX method for agricultural pest detection under unbalanced data. With the expansion of food demand due to rapid population growth in recent years, many crops have been damaged by pests due to frequent natural disasters, which have caused serious damage to farmers’ economic development. As an important biological disaster in agricultural production in China and the world, crop pests and diseases are the biggest cause of sustainable and stable development of agricultural production, with a wide range of species, high impact and high potential for outbreaks becoming its label. Accurate detection of pests is therefore an urgent necessity for the excellent development of the crop industry. In this paper, the binary cross-entropy loss in YOLOX is changed to Focal loss, an attention mechanism is added to its backbone feature extraction network Backbone to make the model more edge-oriented, and a depth-separable convolution is introduced to reduce the number of parameters. The improved pest detection model obtained a recall of 93% and an average accuracy of 87.6%, which can be effectively applied in real life.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127497155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved Dark Channel Prior for Image Defogging 改进暗通道先验图像去雾

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930190

Fan Yang, Yunjie Hu

引用次数: 0

Fuzzy Adaptive Disturbance Compensation Control for Cross Directional Register System 交叉寄存器系统的模糊自适应扰动补偿控制

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930257

Jing Zhang, Tao Xiong, Zicheng Li, Gaoxing Xiao

引用次数: 0

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930173

Qian Zhang, Tao Xu, D. Pi

{"title":"A Similarity Measurement Algorithm for Spacecraft Telemetry Time Series","authors":"Qian Zhang, Tao Xu, D. Pi","doi":"10.1109/AICIT55386.2022.9930173","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930173","url":null,"abstract":"Spacecraft telemetry time series data is the main basis for performance monitoring and real-time status analysis of spacecraft. The similarity measurement of time series is one of the important research areas. Aiming at the shortcomings of existing time series similarity measurement methods, this paper proposes a dynamic time warping algorithm based on adaptive segmentation(ASDTW). Aiming at the problem of excessive computational overhead of the Dynamic Time Warping algorithm(DTW), the algorithm divides the original sequence into several sequence segments, and defines the distance of the sequence segments according to their geometric characteristics. In the dynamic matching stage, sequence segments are used as the basic matching unit to solve the problem that the computation overhead caused by traditional point-by-point matching strategy is too high. Finally, this paper verifies the validity and feasibility of the ASDTW algorithm based on the actual telemetry data of a spacecraft. By comparing with the two baselines, the ASDTW algorithm greatly improves the efficiency of the algorithm under the premise of ensuring the measurement accuracy, and solves the problem of The problem of excessive computational time overhead of the DTW algorithm can more effectively support space mission planning.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114599737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved DV-Hop Algorithm Using Hop Distance Correction and Aquila optimization 基于跳距校正和Aquila优化的DV-Hop改进算法

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930301

Fan Yang, Mingzhu Ding

引用次数: 1

The Combined Filter in Ultra-High Frequency Range for Partial Discharge Measurements 用于局部放电测量的超高频组合滤波器

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930154

Wei Li

引用次数: 0

Oriented object detection based on cross-scale information fusion 基于跨尺度信息融合的定向目标检测

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930256

Chen Li, Tongzhou Zhao, Chengbo Mao, Wei Hu

{"title":"Oriented object detection based on cross-scale information fusion","authors":"Chen Li, Tongzhou Zhao, Chengbo Mao, Wei Hu","doi":"10.1109/AICIT55386.2022.9930256","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930256","url":null,"abstract":"Due to the enormous size discrepancies between classes and within classes, as well as the high degree of resemblance across classes of oriented objects, traditional remote sensing object detection was made difficult. Although coping with huge scale differences and high inter-class similarity was made possible by multi-scale information fusion, the multiscale weight fusion technique neglected the impact of cross-scale on picture semantic feature extraction, leading to subpar detection performance. The performance of the delayed inference was caused by the rotating region proposal network, which produced high-quality ideas while expanding the network’s capacity. In this study, a cross-scale shift oriented object detection method was suggested. First, by creating a feature pyramid network, the multi-layer feature maps were successfully fused. First, the multi-layer feature maps were effectively fused by reconstructing a feature pyramid network. A cross-scale shift module was simultaneously introduced to FPN to enhance the correlation between multi-scale properties. Finally, to raise the quality of the bounding boxes produced, an oriented region proposal network (ORPN) was used. On remote sensing datasets from DOTA-V1.5, the proposed method fared better than the control group in terms of detection accuracy.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126235561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fault Detection of insulator in distribution network Based on YOLOv5s Neural Network 基于YOLOv5s神经网络的配电网绝缘子故障检测

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930315

Zengrui Huang, Shilin Hu, Lei Zhang

引用次数: 1

Research on Fog Ranging Based on Binocular Vision 基于双目视觉的雾测距研究

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930174

Sheng Jing, Liheng Wang, Zhu Jingshan, Liao Shengjie

{"title":"Research on Fog Ranging Based on Binocular Vision","authors":"Sheng Jing, Liheng Wang, Zhu Jingshan, Liao Shengjie","doi":"10.1109/AICIT55386.2022.9930174","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930174","url":null,"abstract":"For the robot to travel in foggy weather, the target feature information is not obvious, which affects the robot’s ranging speed and accuracy. In order to realize the rapid and stable advancement of the robot, a binocular vision ranging detection method for the target under foggy conditions is designed and implemented. First, the dehazing algorithm MSBDN (Multi-Scale Boosted Dehazing Network) is used to dehaze the target image to restore the characteristic information of the target; then, the Zhang Zhengyou calibration method is used to obtain the internal and external parameters of the camera; then, the semi-global matching algorithm SGBM (semi-global-blockmatching) to match pixels to obtain the target disparity map; Use WLS (weighted least squares) filtering to smooth and denoise the initial disparity map to obtain the optimal disparity map. Convert disparity map to depth map. The experimental simulation shows that the method can restore the actual features of the image better in the poor image quality of foggy days, improve the distance measurement accuracy, and meet the requirements of robot travel in foggy days.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125984678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0