Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics最新文献_第5页

Discharge Test of Unmanned Aerial Vehicles and Live High-voltage Wires 无人机与带电高压电线的放电试验

Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics Pub Date : 2022-06-24 DOI: 10.1145/3548608.3559287

Chao Feng, Weike Liu, Xianhui Cao, Zhiwei Jia, Yujing Hu

引用次数: 0

Forecasting of Extreme Weather Intensity Based on Grey Model 基于灰色模型的极端天气强度预测

Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics Pub Date : 2022-06-24 DOI: 10.1145/3548608.3559193

Yong-hong Zhou, Xinyue Wang, Daozhong Zhang

引用次数: 0

A deep learning urban traffic congestion forecast model blending the temporal continuity and periodicity 一种融合时间连续性和周期性的深度学习城市交通拥堵预测模型

Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics Pub Date : 2022-06-24 DOI: 10.1145/3548608.3559271

Bin Mu, Yuxi Huang

引用次数: 0

Research on Technical Support Ability of Communication Equipment Based on Comprehensive Evaluation Method 基于综合评价法的通信设备技术保障能力研究

Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics Pub Date : 2022-06-24 DOI: 10.1145/3548608.3559217

Hua Qin, Haoyan Gong, Junlai Song, Deqin Wang

引用次数: 0

Software Control Flow Anomaly Detection Technology Based On Neural Network 基于神经网络的软件控制流异常检测技术

Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics Pub Date : 2022-06-24 DOI: 10.1145/3548608.3559263

Xinda Xu, Jingling Zhao, Baojiang Cui

{"title":"Software Control Flow Anomaly Detection Technology Based On Neural Network","authors":"Xinda Xu, Jingling Zhao, Baojiang Cui","doi":"10.1145/3548608.3559263","DOIUrl":"https://doi.org/10.1145/3548608.3559263","url":null,"abstract":"This paper presents a control flow anomaly detection model, which applies neural network to control flow anomaly detection and performs feature extraction and behavior modeling of control flow. At present, there is little research on the control flow anomaly detection of neural networks, and there is no in-depth research on the feature extraction of data. We studied the characteristics of control flow, used Intel Processor Trace to implement the extraction and processing of control flow, and designed a basic block vectorization method based on time series features and a basic block vectorization method based on structural features. The vectorization methods eliminate the manual amount of feature engineering. The anomaly detection model uses a bidirectional LSTM and it combines the idea of a classification plane. We perform corresponding evaluations based on the adobe reader software. Experimental results show that the model achieves a 98.74% recall rate and a 0.44% false positive rate for the corresponding control flow anomaly detection of Adobe Reader in an offline environment, effectively detects the exploit, and successfully distinguishes between benign and malicious control flow.","PeriodicalId":201434,"journal":{"name":"Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115954580","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

Vehicle detection algorithm based on multi-scale features and normalization attention model 基于多尺度特征和规范化注意力模型的车辆检测算法

Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics Pub Date : 2022-06-24 DOI: 10.1145/3548608.3559196

Yu-Shuai Duan, Huarong Xu, Lifen Weng

{"title":"Vehicle detection algorithm based on multi-scale features and normalization attention model","authors":"Yu-Shuai Duan, Huarong Xu, Lifen Weng","doi":"10.1145/3548608.3559196","DOIUrl":"https://doi.org/10.1145/3548608.3559196","url":null,"abstract":"As the key technology of automatic driving perception module, vehicle detection in complex scenes requires real-time and accurate acquisition of the position and distance information of surrounding vehicles, so as to ensure the safety of passengers. Centernet algorithm performs well in vehicle detection, achieving a trade-off between accuracy and speed, but the network only extracts features of the target at the last layer of the feature map, leading to the problem of missed and false detections during detection. Therefore, this paper proposes a Vehicle-CenterNet detection model, which obtains more detailed information by modifying the original ResNet, constructing layered connections within a single residual block, and increasing the perceptual field size of each layer by stacking convolution operators. In addition, the Mish activation function is used instead of the ReLU activation function, and the smoothed activation function allows better information penetration into the neural network, resulting in better accuracy and generalization. The normalization-based attention module (NAM) is also incorporated to suppress non-target features and further improve the detection accuracy of the model. Experimental results on VOC dataset and KITTI dataset show that the mean average precision (mAP) and F1 Score of the proposed method are improved to different degrees, and the comprehensive performance is better than the original CenterNet algorithm.","PeriodicalId":201434,"journal":{"name":"Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126466257","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

Road Vehicle Detection Based on Feature Fusion Between Frames 基于帧间特征融合的道路车辆检测

Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics Pub Date : 2022-06-24 DOI: 10.1145/3548608.3559277

Xinbo Ai, Fu Gong, Yingjian Wang, Yanjun Guo

{"title":"Road Vehicle Detection Based on Feature Fusion Between Frames","authors":"Xinbo Ai, Fu Gong, Yingjian Wang, Yanjun Guo","doi":"10.1145/3548608.3559277","DOIUrl":"https://doi.org/10.1145/3548608.3559277","url":null,"abstract":"With the rapid economic development, motor vehicles are becoming more popular, and artificial intelligence applications on the road are emerging in endlessly. In current road vehicle detection algorithms, most of them use single-frame image information intercepted from video sequences for vehicle detection. This method does not take into account that the difference between frames in the video sequence is mainly the motion background information. Aiming at this design limitation, this paper proposes a target detection method based on IFFF (Inter-Frame Feature Fusion). In the input part of the model, in addition to adding the picture of the current frame, the feature map output of the previous frame will be added to enrich the information of the current frame and improve the detection performance of the current frame. At the same time, a spatial pyramid pooling structure is added to the network to further integrate local and global features to improve the ability to detect vehicles. Experiments show that the method proposed in this paper can improve the detection effect of vehicles in road scenes. Compared with the original CenterNet detection network, the mAP index is improved by 4.3%.","PeriodicalId":201434,"journal":{"name":"Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127475181","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

SER-UNet: A Network for Gastrointestinal Image Segmentation SER-UNet:胃肠图像分割网络

Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics Pub Date : 2022-06-24 DOI: 10.1145/3548608.3559197

Hongwei Niu, Yutong Lin

引用次数: 3

Research on fingertip positioning and human-computer interaction technology based on stereo vision 基于立体视觉的指尖定位与人机交互技术研究

Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics Pub Date : 2022-06-24 DOI: 10.1145/3548608.3559178

Guoquan Cong

引用次数: 0

Deep learning based recyclable waste classification 基于深度学习的可回收垃圾分类

Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics Pub Date : 2022-06-24 DOI: 10.1145/3548608.3559190

Yulong He, Tianjian Li, Jianchao Huang, Zejun Zhang, Zhuangzhuang Wang, Zhiming Cai

引用次数: 0