Deep learning has achieved great success in many fields, such as image classification and target detection. Adding small disturbance which is hard to be detected by the human eyes to original images can make the neural network output error results with high confidence. An image after adding small disturbance is an adversarial example. The existence of adversarial examples brings a huge security problem to deep learning. In order to effectively defend against adversarial examples attacks, an adversarial example defense method based on image reconstruction is proposed by analyzing the existing adversarial examples attack methods and defense methods. Our data set is based on ImageNet 1k data set, and some filtering and expansion are carried out. Four attack modes, FGSM, BIM, DeepFool and C&W are selected to test the defense method. Based on the EDSR network, multi-scale feature fusion module and subspace attention module are added. By capturing the global correlation information of the image, the disturbance can be removed, while the image texture details can be better preserved, and the defense performance can be improved. The experimental results show that the proposed method has good defense effect.
{"title":"A neural network model for adversarial defense based on deep learning","authors":"Zhiying Wang, Yong Wang","doi":"10.1117/12.3000789","DOIUrl":"https://doi.org/10.1117/12.3000789","url":null,"abstract":"Deep learning has achieved great success in many fields, such as image classification and target detection. Adding small disturbance which is hard to be detected by the human eyes to original images can make the neural network output error results with high confidence. An image after adding small disturbance is an adversarial example. The existence of adversarial examples brings a huge security problem to deep learning. In order to effectively defend against adversarial examples attacks, an adversarial example defense method based on image reconstruction is proposed by analyzing the existing adversarial examples attack methods and defense methods. Our data set is based on ImageNet 1k data set, and some filtering and expansion are carried out. Four attack modes, FGSM, BIM, DeepFool and C&W are selected to test the defense method. Based on the EDSR network, multi-scale feature fusion module and subspace attention module are added. By capturing the global correlation information of the image, the disturbance can be removed, while the image texture details can be better preserved, and the defense performance can be improved. The experimental results show that the proposed method has good defense effect.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126726056","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}
In response to the problem that the current image processing technology and underwater target recognition algorithms are not yet mature enough in the field of underwater archaeology, this article innovatively applies object detection and underwater image clarity technology to the field of underwater archaeology. We propose a method for detecting and recognizing underwater cultural heritage based on optical devices. The method includes ocean image preprocessing and underwater cultural heritage object recognition based on YOLO V4. The results of experiments demonstrate that the proposed method can effectively and accurately detect and recognize targets in the underwater cultural heritage scene, and the clear image of the underwater relics after image preprocessing can better assist archaeologists in observing the species and distribution of samples in the real scene.
{"title":"Research on optical detection technology for underwater archaeology","authors":"Wei Mu, Ruohan Zheng, Wenrui Zhang","doi":"10.1117/12.3002208","DOIUrl":"https://doi.org/10.1117/12.3002208","url":null,"abstract":"In response to the problem that the current image processing technology and underwater target recognition algorithms are not yet mature enough in the field of underwater archaeology, this article innovatively applies object detection and underwater image clarity technology to the field of underwater archaeology. We propose a method for detecting and recognizing underwater cultural heritage based on optical devices. The method includes ocean image preprocessing and underwater cultural heritage object recognition based on YOLO V4. The results of experiments demonstrate that the proposed method can effectively and accurately detect and recognize targets in the underwater cultural heritage scene, and the clear image of the underwater relics after image preprocessing can better assist archaeologists in observing the species and distribution of samples in the real scene.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115529067","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}
There are still challenges in the field of video understanding today, especially how to use natural language to describe the visual content in videos. Existing video encoder-decoder models struggle to extract deep semantic information and effectively understand the complex contextual semantics in a video sequence. Furthermore, different visual elements in the video contribute differently to the generation of video text descriptions. In this paper, we propose a video description method that fuses instance-aware temporal features. We extract local features of instances on the temporal sequence to enhance perception of temporal instances. We also employ spatial attention to perform weighted fusion of temporal features. Finally, we use bidirectional long short-term memory networks to encode the contextual semantic information of the video sequence, thereby helping to generate higher quality descriptive text. Experimental results on two public datasets demonstrate that our method achieves good performance on various evaluation metrics.
{"title":"Video description method with fusion of instance-aware temporal features","authors":"Ju Huang, He Yan, Lingkun Liu, Yuhan Liu","doi":"10.1117/12.3000765","DOIUrl":"https://doi.org/10.1117/12.3000765","url":null,"abstract":"There are still challenges in the field of video understanding today, especially how to use natural language to describe the visual content in videos. Existing video encoder-decoder models struggle to extract deep semantic information and effectively understand the complex contextual semantics in a video sequence. Furthermore, different visual elements in the video contribute differently to the generation of video text descriptions. In this paper, we propose a video description method that fuses instance-aware temporal features. We extract local features of instances on the temporal sequence to enhance perception of temporal instances. We also employ spatial attention to perform weighted fusion of temporal features. Finally, we use bidirectional long short-term memory networks to encode the contextual semantic information of the video sequence, thereby helping to generate higher quality descriptive text. Experimental results on two public datasets demonstrate that our method achieves good performance on various evaluation metrics.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114131405","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}
In order to solve the multi-vehicle mutual occlusion problem encountered in 3D target detection by self-driving vehicles, this paper proposes a monocular 3D detection method that includes dynamic occlusion determination. The method adds a dynamic occlusion processing module to the CenterNet3D network framework to improve the accuracy of 3D target detection of occluded vehicles in the road. Specifically, the occlusion determination module of the method uses the 2D detection results extracted from target detection as the occlusion relationship determination condition, wherein the method of changing the occlusion determination threshold with the depth value is introduced. Then the occlusion compensation module is used to compensate and adjust the 3D detection results of the occurring occluded vehicles, and finally the 3D target detection results are output. The experimental results show that the method improves the accuracy of both vehicle center point detection and 3D dimensional detection results in the case of long-distance continuous vehicle occlusion. And compared with other existing methods, the accuracy of 3D detection results and bird's-eye view detection results are improved by 1%-2.64% in the case of intersection over union of 0.5. The method can compensate for the occluded vehicles in 3D target detection and improve the accuracy
{"title":"3D target detection based on dynamic occlusion processing","authors":"Jishen Peng, Jun Ma, Li Li","doi":"10.1117/12.3000786","DOIUrl":"https://doi.org/10.1117/12.3000786","url":null,"abstract":"In order to solve the multi-vehicle mutual occlusion problem encountered in 3D target detection by self-driving vehicles, this paper proposes a monocular 3D detection method that includes dynamic occlusion determination. The method adds a dynamic occlusion processing module to the CenterNet3D network framework to improve the accuracy of 3D target detection of occluded vehicles in the road. Specifically, the occlusion determination module of the method uses the 2D detection results extracted from target detection as the occlusion relationship determination condition, wherein the method of changing the occlusion determination threshold with the depth value is introduced. Then the occlusion compensation module is used to compensate and adjust the 3D detection results of the occurring occluded vehicles, and finally the 3D target detection results are output. The experimental results show that the method improves the accuracy of both vehicle center point detection and 3D dimensional detection results in the case of long-distance continuous vehicle occlusion. And compared with other existing methods, the accuracy of 3D detection results and bird's-eye view detection results are improved by 1%-2.64% in the case of intersection over union of 0.5. The method can compensate for the occluded vehicles in 3D target detection and improve the accuracy","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114918538","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}
In order to show the transport conditions of goods on different roads and provide more real and three-dimensional transport information for situation inference users, this paper proposes a simple and PID controlled three-dimensional simulation method for truck formation based on Unreal Engine. Firstly, based on the basic theory of automatic control [1] , the longitudinal lollipop controller and the transverse PID controller are designed respectively based on the lollipop control and PID control ideas, and the perception-decision framework is combined to realize the automatic driving of the truck along the spline line on the road. On this basis, a truck controller is designed to realize the truck formation driving with high recovery degree based on the leader-follower strategy. The results show that the truck based on PID control can accurately drive along the road line. With the cooperation of truck formation controller, the whole process of formation, maintenance and driving of truck formation can be basically restored.
{"title":"Research and application of 3D simulation of truck formation based on Unreal Engine","authors":"Zhenzhou Wang, Fang Wu, Jiangnan Zhang, Jianguang Wu","doi":"10.1117/12.3001392","DOIUrl":"https://doi.org/10.1117/12.3001392","url":null,"abstract":"In order to show the transport conditions of goods on different roads and provide more real and three-dimensional transport information for situation inference users, this paper proposes a simple and PID controlled three-dimensional simulation method for truck formation based on Unreal Engine. Firstly, based on the basic theory of automatic control [1] , the longitudinal lollipop controller and the transverse PID controller are designed respectively based on the lollipop control and PID control ideas, and the perception-decision framework is combined to realize the automatic driving of the truck along the spline line on the road. On this basis, a truck controller is designed to realize the truck formation driving with high recovery degree based on the leader-follower strategy. The results show that the truck based on PID control can accurately drive along the road line. With the cooperation of truck formation controller, the whole process of formation, maintenance and driving of truck formation can be basically restored.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128967080","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}
In this paper, the non-cultural material heritage mobile terminal APP interface is the carrier, according to the user experience of the interactive interface design. By using user interview, observation, qualitative research and quantitative research, and based on the theoretical model of user experience, the author conducted data collection and analysis using user interview and questionnaire survey to obtain four evaluation indexes and eight sub-criteria for users' interaction interface of intangible cultural heritage apps. The analytic hierarchy process was introduced into weight calculation. The weight of each evaluation factor is obtained through investigation and calculation, and the evaluation level of each element is determined by referring to the Likert scale. The evaluation data of the design scheme is obtained through the questionnaire method, the fuzzy analysis is carried out on the results of the questionnaire, and the final evaluation results are obtained according to the principle of full membership to provide implementable improvement suggestions for the interactive interface design to improve the user experience. The research results have theoretical guiding significance for the interactive interface design of intangible cultural heritage apps.
{"title":"Evaluation of design factors of an interactive interface of intangible cultural heritage APP based on user experience","authors":"Chengjun Zhou, Ruowei Li","doi":"10.1117/12.3000771","DOIUrl":"https://doi.org/10.1117/12.3000771","url":null,"abstract":"In this paper, the non-cultural material heritage mobile terminal APP interface is the carrier, according to the user experience of the interactive interface design. By using user interview, observation, qualitative research and quantitative research, and based on the theoretical model of user experience, the author conducted data collection and analysis using user interview and questionnaire survey to obtain four evaluation indexes and eight sub-criteria for users' interaction interface of intangible cultural heritage apps. The analytic hierarchy process was introduced into weight calculation. The weight of each evaluation factor is obtained through investigation and calculation, and the evaluation level of each element is determined by referring to the Likert scale. The evaluation data of the design scheme is obtained through the questionnaire method, the fuzzy analysis is carried out on the results of the questionnaire, and the final evaluation results are obtained according to the principle of full membership to provide implementable improvement suggestions for the interactive interface design to improve the user experience. The research results have theoretical guiding significance for the interactive interface design of intangible cultural heritage apps.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114128327","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}
The actual underground situation is of great significance for workover operation. Videolog visualization technology can clearly and accurately obtain the underground color video information, and provide effective guidance for workover operation. This paper introduces the system composition, working principle and functional parameters of Videolog equipment, and gives an example of its practical application in workover operation, which shows that Videolog visualization technology is more efficient, safe and intuitive than traditional downhole video technology, and has a good application prospect in workover operation field.
{"title":"Application of Videolog visualization technology in workover operation","authors":"Ying Zhang, Jiatian Zhang, Wenhao Jin","doi":"10.1117/12.3001436","DOIUrl":"https://doi.org/10.1117/12.3001436","url":null,"abstract":"The actual underground situation is of great significance for workover operation. Videolog visualization technology can clearly and accurately obtain the underground color video information, and provide effective guidance for workover operation. This paper introduces the system composition, working principle and functional parameters of Videolog equipment, and gives an example of its practical application in workover operation, which shows that Videolog visualization technology is more efficient, safe and intuitive than traditional downhole video technology, and has a good application prospect in workover operation field.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116248229","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}
This paper studies feature extraction and feature matching in visual odometry. Aiming at the problems that ORB feature extraction does not have illumination invariance and feature distribution is uneven, an adaptive threshold algorithm for feature extraction is added, and a quadtree is used to manage feature points. Aiming at the problem of high time cost of the feature matching algorithm, an outlier removal algorithm based on geometric constraints is proposed, and the constraint set is constructed by using the slope, distance, and descriptor distance between the matching feature point pairs. Tested on the TUM dataset, the feature extraction algorithm can adapt to scenes with different brightness, and the robustness is improved. The time taken by outlier removal algorithm based on geometric constraints is about 10% of RANSAC. After that, combined with RANSAC, the running time of RANSAC can be reduced by 60%. Our algorithm can improve the estimation accuracy and robustness of the system.
{"title":"ORB feature extraction and feature matching based on geometric constraints","authors":"Zhenyu Wu, Xueqian Wu","doi":"10.1117/12.3000969","DOIUrl":"https://doi.org/10.1117/12.3000969","url":null,"abstract":"This paper studies feature extraction and feature matching in visual odometry. Aiming at the problems that ORB feature extraction does not have illumination invariance and feature distribution is uneven, an adaptive threshold algorithm for feature extraction is added, and a quadtree is used to manage feature points. Aiming at the problem of high time cost of the feature matching algorithm, an outlier removal algorithm based on geometric constraints is proposed, and the constraint set is constructed by using the slope, distance, and descriptor distance between the matching feature point pairs. Tested on the TUM dataset, the feature extraction algorithm can adapt to scenes with different brightness, and the robustness is improved. The time taken by outlier removal algorithm based on geometric constraints is about 10% of RANSAC. After that, combined with RANSAC, the running time of RANSAC can be reduced by 60%. Our algorithm can improve the estimation accuracy and robustness of the system.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121496173","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}
The situation of the future naval battlefield will become more and more complex, and it will become a trend to develop various military auxiliary decision-making systems based on artificial intelligence and big data technology. This paper sorts out the key technologies of the auxiliary decision-making system based on deep reinforcement learning. On this basis, it proposes the construction method of the naval aviation sea-striking agent model, and completes the construction of the training framework with the combat deduction system as the environment. Finally, it summarizes and prospects some of future work.
{"title":"Research on auxiliary decision-making for sea striking of naval aviation based on deep reinforcement learning","authors":"Minjie Wu, D. Yin","doi":"10.1117/12.3000933","DOIUrl":"https://doi.org/10.1117/12.3000933","url":null,"abstract":"The situation of the future naval battlefield will become more and more complex, and it will become a trend to develop various military auxiliary decision-making systems based on artificial intelligence and big data technology. This paper sorts out the key technologies of the auxiliary decision-making system based on deep reinforcement learning. On this basis, it proposes the construction method of the naval aviation sea-striking agent model, and completes the construction of the training framework with the combat deduction system as the environment. Finally, it summarizes and prospects some of future work.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133063780","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}
YOLOv5 is one of the target detection algorithms with fast detection speed and high accuracy, but it has the problems of insufficient sensory field and low accuracy of small target detection. In order to solve above problems, an improved YOLOv5 network model, i.e., an improved YOLOv5-TI model based on the attention mechanism, is proposed. The attention module is added to the backbone network when extracting features to improve the target detection accuracy, and the input features are shifted windowed for self-attention calculation to effectively utilize the features and improve the small target detection accuracy; the proposed model YOLOv5-TI is experimented on the self-built inland infrared dataset, and the mAP value reaches 95.5%, and the results show that YOLOv5-TI can effectively improve the target detection accuracy. The inland vessels equipped with visual intelligent perception system can effectively identify the targets on water, and they have wide applications in the fields of surface exploration and autonomous search and rescue.
{"title":"Infrared small target recognition in waterways based on YOLOv5 algorithm","authors":"Yikai Fan, Yingjun Zhang","doi":"10.1117/12.3002081","DOIUrl":"https://doi.org/10.1117/12.3002081","url":null,"abstract":"YOLOv5 is one of the target detection algorithms with fast detection speed and high accuracy, but it has the problems of insufficient sensory field and low accuracy of small target detection. In order to solve above problems, an improved YOLOv5 network model, i.e., an improved YOLOv5-TI model based on the attention mechanism, is proposed. The attention module is added to the backbone network when extracting features to improve the target detection accuracy, and the input features are shifted windowed for self-attention calculation to effectively utilize the features and improve the small target detection accuracy; the proposed model YOLOv5-TI is experimented on the self-built inland infrared dataset, and the mAP value reaches 95.5%, and the results show that YOLOv5-TI can effectively improve the target detection accuracy. The inland vessels equipped with visual intelligent perception system can effectively identify the targets on water, and they have wide applications in the fields of surface exploration and autonomous search and rescue.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130373002","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}
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