Using the new technologies such as information technology, communication technology and electronic control technology, vehicle collision warning system（CWS） can acquire road condition, adjacent vehicle march conditi...Using the new technologies such as information technology, communication technology and electronic control technology, vehicle collision warning system（CWS） can acquire road condition, adjacent vehicle march condition as well as its dynamics performance continuously, then it can forecast the oncoming potential collision and give a warning. Based on the analysis of driver＇s driving behavior, algorithm＇s warning norms are determined. Based on warning norms adopting machine vision method, the cooperation collision warning algorithm（CWA） model with multi-input and multi-output is established which is used in supporting vehicle CWS. The CWA is tested using the actual data and the result shows that this algorithm can identify and carry out warning for vehicle collision efficiently, which has important meaning for improving the vehicle travel safety.展开更多
Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a ...Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.展开更多
6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is...6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.展开更多
目的对抗样本作为人工智能安全领域的基础性问题,其生成方法的研究具有重要意义。当前的全局扰动优化策略生成的对抗样本存在源模型过拟合问题,这显著降低了跨模型迁移攻击的效果。为了解决这一问题,一种有效的思路是将图像转换到频率域...目的对抗样本作为人工智能安全领域的基础性问题,其生成方法的研究具有重要意义。当前的全局扰动优化策略生成的对抗样本存在源模型过拟合问题,这显著降低了跨模型迁移攻击的效果。为了解决这一问题,一种有效的思路是将图像转换到频率域,提取对决策相关的频率信息后进行针对性干扰以生成对抗样本。但现有频率域攻击方法过度依赖低频信息,导致对抗攻击的可迁移性受限。方法为了突破传统攻击方法在迁移性与生成效率方面的双重局限,本文提出一种基于生成器架构的频率域对抗样本生成方法。该方法能够在频率域中自适应地保留对分类决策有益的高频和低频信息,并通过干扰这些信息生成对抗样本。通过利用生成器架构,本文方法无需真实标签,一旦生成器训练完成,即可直接将输入图像送入生成器生成对抗样本,从而显著提高生成效率,同时保持了较高的迁移攻击性能。结果在ImageNet上进行的大量实验证明,本方法在卷积神经网络(convolutional neural network,CNN)和视觉变换器(vision Transformer,ViT)上均优于现有方法。此外,本文还利用了动物数据集以及微软通用物体检测数据集(Microsoft common objects in context,MS-COCO)分别在跨数据集图像分类任务以及目标检测任务上验证了攻击方法的有效性。结论本文提出的基于生成器架构的频率域对抗样本生成方法,通过自适应选择并干扰对分类决策有益的频率成分,显著提升了对抗样本的迁移攻击能力,在生成效率和攻击性能上均优于对比方法,为对抗样本生成领域提供了新的解决方案。展开更多
基金Sponsored by the Special Development Foundation of High School’s Doctor Subject of China (20030006007)
文摘Using the new technologies such as information technology, communication technology and electronic control technology, vehicle collision warning system（CWS） can acquire road condition, adjacent vehicle march condition as well as its dynamics performance continuously, then it can forecast the oncoming potential collision and give a warning. Based on the analysis of driver＇s driving behavior, algorithm＇s warning norms are determined. Based on warning norms adopting machine vision method, the cooperation collision warning algorithm（CWA） model with multi-input and multi-output is established which is used in supporting vehicle CWS. The CWA is tested using the actual data and the result shows that this algorithm can identify and carry out warning for vehicle collision efficiently, which has important meaning for improving the vehicle travel safety.
文摘Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.
基金supported by the Inner Mongolia Natural Science Fund Project(2019MS06013)Ordos Science and Technology Plan Project(2022YY041)Hunan Enterprise Science and Technology Commissioner Program(2021GK5042).
文摘6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.
文摘目的对抗样本作为人工智能安全领域的基础性问题,其生成方法的研究具有重要意义。当前的全局扰动优化策略生成的对抗样本存在源模型过拟合问题,这显著降低了跨模型迁移攻击的效果。为了解决这一问题,一种有效的思路是将图像转换到频率域,提取对决策相关的频率信息后进行针对性干扰以生成对抗样本。但现有频率域攻击方法过度依赖低频信息,导致对抗攻击的可迁移性受限。方法为了突破传统攻击方法在迁移性与生成效率方面的双重局限,本文提出一种基于生成器架构的频率域对抗样本生成方法。该方法能够在频率域中自适应地保留对分类决策有益的高频和低频信息,并通过干扰这些信息生成对抗样本。通过利用生成器架构,本文方法无需真实标签,一旦生成器训练完成,即可直接将输入图像送入生成器生成对抗样本,从而显著提高生成效率,同时保持了较高的迁移攻击性能。结果在ImageNet上进行的大量实验证明,本方法在卷积神经网络(convolutional neural network,CNN)和视觉变换器(vision Transformer,ViT)上均优于现有方法。此外,本文还利用了动物数据集以及微软通用物体检测数据集(Microsoft common objects in context,MS-COCO)分别在跨数据集图像分类任务以及目标检测任务上验证了攻击方法的有效性。结论本文提出的基于生成器架构的频率域对抗样本生成方法,通过自适应选择并干扰对分类决策有益的频率成分,显著提升了对抗样本的迁移攻击能力,在生成效率和攻击性能上均优于对比方法,为对抗样本生成领域提供了新的解决方案。
