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":"39 1","pages":"0"},"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":"6 1","pages":"0"},"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":"12782 1","pages":"0"},"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}
The conventional discharge state identification method mainly focuses on partial identification. The field identification environment is subject to various interference signals from Getang, resulting in poor performance of the ring main unit discharge state identification. Therefore, an intelligent ring network cabinet discharge state recognition method based on audio signal spectrum analysis is designed. Collect the partial discharge data of the intelligent ring network cabinet, and extract the characteristics of the partial discharge of the intelligent ring network cabinet. Based on the audio signal spectrum analysis, the partial discharge noise signal of the ring main unit is processed, and the discharge noise signal is filtered to ensure accurate identification of the discharge signal. By means of comparative experiments, it is verified that the recognition effect of this method is better and can be applied to real life.
{"title":"Discharging state recognition method of intelligent ring network cabinet based on audio signal spectrum analysis","authors":"Mingming Zhang, Jin Hu, Wenjun Li","doi":"10.1117/12.3000839","DOIUrl":"https://doi.org/10.1117/12.3000839","url":null,"abstract":"The conventional discharge state identification method mainly focuses on partial identification. The field identification environment is subject to various interference signals from Getang, resulting in poor performance of the ring main unit discharge state identification. Therefore, an intelligent ring network cabinet discharge state recognition method based on audio signal spectrum analysis is designed. Collect the partial discharge data of the intelligent ring network cabinet, and extract the characteristics of the partial discharge of the intelligent ring network cabinet. Based on the audio signal spectrum analysis, the partial discharge noise signal of the ring main unit is processed, and the discharge noise signal is filtered to ensure accurate identification of the discharge signal. By means of comparative experiments, it is verified that the recognition effect of this method is better and can be applied to real life.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115440246","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}
Football is a high-intensity sport that requires high physical fitness from athletes. Physical fitness is a key element that every football player must master. This article aimed to design an information technology based football player physical fitness video monitoring system, which can provide coaches with detailed information about the athlete’s physical condition and help them develop training plans. This article mainly used experimental design and data comparison to analyze the pre and post monitoring data of football players. The experimental data showed that the speed standard deviation of athletes before and after monitoring was below 0.1 at 10m and 5m. A football player physical fitness video monitoring system based on information technology is a feasible way to provide coaches with detailed information about their physical condition and help them develop training plans.
{"title":"Exploration on the football player physical fitness video monitoring system based on information technology","authors":"Xuchao Liu, Zixiao Gong","doi":"10.1117/12.3000798","DOIUrl":"https://doi.org/10.1117/12.3000798","url":null,"abstract":"Football is a high-intensity sport that requires high physical fitness from athletes. Physical fitness is a key element that every football player must master. This article aimed to design an information technology based football player physical fitness video monitoring system, which can provide coaches with detailed information about the athlete’s physical condition and help them develop training plans. This article mainly used experimental design and data comparison to analyze the pre and post monitoring data of football players. The experimental data showed that the speed standard deviation of athletes before and after monitoring was below 0.1 at 10m and 5m. A football player physical fitness video monitoring system based on information technology is a feasible way to provide coaches with detailed information about their physical condition and help them develop training plans.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"317 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123156888","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}
Aiming at the problem of low detection accuracy in traditional UAV target recognition, an improved YOLOv5 target recognition method is proposed. The loss function of YOLOv5 is improved, and the CIoU loss function is used instead of the GIoU loss function used by YOLOv5 to optimize the training model. The accuracy of the algorithm is improved, and a more accurate identification of the target is realized. The experimental results show that the mAP value of the model trained on the aviation dataset NWPU VHR-10 by the improved YOLOv5 algorithm reaches 93.33%, which is 4% higher than the original YOLOv5 algorithm.
{"title":"Research on target recognition technology based on improved YOLOv5","authors":"Lu-lu Fang, Yang Zhang, Tao Jing, Hai Hu","doi":"10.1117/12.3000843","DOIUrl":"https://doi.org/10.1117/12.3000843","url":null,"abstract":"Aiming at the problem of low detection accuracy in traditional UAV target recognition, an improved YOLOv5 target recognition method is proposed. The loss function of YOLOv5 is improved, and the CIoU loss function is used instead of the GIoU loss function used by YOLOv5 to optimize the training model. The accuracy of the algorithm is improved, and a more accurate identification of the target is realized. The experimental results show that the mAP value of the model trained on the aviation dataset NWPU VHR-10 by the improved YOLOv5 algorithm reaches 93.33%, which is 4% higher than the original YOLOv5 algorithm.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123688146","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 continuous advancement of artificial intelligence technology has made autonomous driving possible. However, duo the lack of sufficient data to train a good deep learning model, the current smart driving system can only rely on the driver for autonomous control, which may have serious consequences in the event of an accident. In practical applications, smart driving systems not only need autonomous driving technology, but must also be able to recognize obstacles and accurately avoid them without relying on manual manipulation, making the integration of autonomous driving features into vehicles a very promising research direction. To address this problem, we propose a novel segmentation method, AU-Net, which is capable of achieving accurate and complete segmentation of complex scenes by introducing an axial attention mechanism. We evaluate the performance of our model on the dataset Camvid, which improves 0.54%, 0.47%, 0.32% and 1.54% in the miaou, accuracy, percision and recall metrics, respectively, and the results show that our model is well adapted to complex scenes in intelligent driving detection.
{"title":"AU-Net: an image segmentation for complex scenes","authors":"Xiao Dai, Xiaoyu Li, Bei Yu","doi":"10.1117/12.3001288","DOIUrl":"https://doi.org/10.1117/12.3001288","url":null,"abstract":"The continuous advancement of artificial intelligence technology has made autonomous driving possible. However, duo the lack of sufficient data to train a good deep learning model, the current smart driving system can only rely on the driver for autonomous control, which may have serious consequences in the event of an accident. In practical applications, smart driving systems not only need autonomous driving technology, but must also be able to recognize obstacles and accurately avoid them without relying on manual manipulation, making the integration of autonomous driving features into vehicles a very promising research direction. To address this problem, we propose a novel segmentation method, AU-Net, which is capable of achieving accurate and complete segmentation of complex scenes by introducing an axial attention mechanism. We evaluate the performance of our model on the dataset Camvid, which improves 0.54%, 0.47%, 0.32% and 1.54% in the miaou, accuracy, percision and recall metrics, respectively, and the results show that our model is well adapted to complex scenes in intelligent driving detection.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116642169","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 plan and design different models of urban landscape, this paper presents numerical methods and calculation methods for different models of urban landscape. Through the analysis of urban green space landscape pattern, it can be found that there are still problems such as the incompatibility of green space and space distribution, the dispersal of some green space, the high fragmentation space, and the urban green space shortage. In the future urban green space development, attention should be paid to the following problems: there is less green space in the central part of the city, and the green space distribution is focused on the poor areas, especially in the northwest part of the city. In future urban development, especially in the context of old urban development, it is necessary to strengthen the construction of green space in the central region, appropriately provide some economic benefits, and increase the green space in the central region of the city by destruction, the construction of small parks and green space, so that the public can live together. Urban ecological green space is insufficient, and construction should be strengthened. City A has good natural resources, among which the ancient Yellow River and the Beijing-Hangzhou Grand Canal pass through the city, and the urban water network is developed. We should focus on building ecological green spaces on both sides of the river and reduce the construction projects of residential quarters within a certain buffer zone. At present, there are many other green spaces in urban green space, most of which are unused construction land or green space, which may be occupied in the process of urban development. How to ensure the quantity and quality of such green spaces will be the key issues for urban builders.
{"title":"Numerical analysis and calculation of urban landscape spatial pattern","authors":"Fan Zhang, Jialin Li","doi":"10.1117/12.3000938","DOIUrl":"https://doi.org/10.1117/12.3000938","url":null,"abstract":"In order to plan and design different models of urban landscape, this paper presents numerical methods and calculation methods for different models of urban landscape. Through the analysis of urban green space landscape pattern, it can be found that there are still problems such as the incompatibility of green space and space distribution, the dispersal of some green space, the high fragmentation space, and the urban green space shortage. In the future urban green space development, attention should be paid to the following problems: there is less green space in the central part of the city, and the green space distribution is focused on the poor areas, especially in the northwest part of the city. In future urban development, especially in the context of old urban development, it is necessary to strengthen the construction of green space in the central region, appropriately provide some economic benefits, and increase the green space in the central region of the city by destruction, the construction of small parks and green space, so that the public can live together. Urban ecological green space is insufficient, and construction should be strengthened. City A has good natural resources, among which the ancient Yellow River and the Beijing-Hangzhou Grand Canal pass through the city, and the urban water network is developed. We should focus on building ecological green spaces on both sides of the river and reduce the construction projects of residential quarters within a certain buffer zone. At present, there are many other green spaces in urban green space, most of which are unused construction land or green space, which may be occupied in the process of urban development. How to ensure the quantity and quality of such green spaces will be the key issues for urban builders.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132451497","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}
Improved fuzzy c-means (FCM) clustering algorithms have been widely used for image recognition and localization. However, in industrial assembly systems, the unsatisfactory pixel merging and segmentation results between local adjacent windows, combined with the differences in the shape, size, and material of parts, as well as variations in lighting conditions, make target image recognition and localization a challenge. Most algorithms struggle to achieve the expected results and have high computational complexity. In this study, we propose a super-resolution-based FCM clustering algorithm that is faster and more accurate for image recognition and localization in industrial assembly systems with irregular part sizes. We first use multiscale morphological gradient operations to obtain high-resolution images. Then, we use the fast FCM clustering algorithm to achieve the recognition and extraction of specific target images. Finally, we use the Sobel operator to determine the target's position. The experimental results demonstrate that the proposed algorithm shows higher accuracy and efficiency in image recognition and localization for industrial assembly systems.
{"title":"Image recognition and position technology based on super-pixel fuzzy C-means clustering in industrial assembly systems","authors":"Hailiang Yuan, Weitao Sun, Hailing Wang","doi":"10.1117/12.3001356","DOIUrl":"https://doi.org/10.1117/12.3001356","url":null,"abstract":"Improved fuzzy c-means (FCM) clustering algorithms have been widely used for image recognition and localization. However, in industrial assembly systems, the unsatisfactory pixel merging and segmentation results between local adjacent windows, combined with the differences in the shape, size, and material of parts, as well as variations in lighting conditions, make target image recognition and localization a challenge. Most algorithms struggle to achieve the expected results and have high computational complexity. In this study, we propose a super-resolution-based FCM clustering algorithm that is faster and more accurate for image recognition and localization in industrial assembly systems with irregular part sizes. We first use multiscale morphological gradient operations to obtain high-resolution images. Then, we use the fast FCM clustering algorithm to achieve the recognition and extraction of specific target images. Finally, we use the Sobel operator to determine the target's position. The experimental results demonstrate that the proposed algorithm shows higher accuracy and efficiency in image recognition and localization for industrial assembly systems.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127366476","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}
Hao Sun, Linwei Dong, Xuhang Huang, Yuqi Fan, Yupeng Mei
UAV clusters are a new type of cluster mode, which should maintain electromagnetic silence as much as possible during the formation flight. When the deviation of the UAVs position occurs during the flight, a pure direction finding method can be used to adjust the queue position of the UAVs. In this paper, we model and analyze the problem of position deviation for UAVs based on pure direction finding by optimizing theory and grid convenience method. Firstly, we establish an unbiased positioning model of the transmitter under ideal conditions. Then, we modify the model by equivalent conversion of deviation and establish the final biased positioning model of the transmitter. Finally, we simulate the actual UAV positioning situation through MATLAB simulation and verify the feasibility of our model.
{"title":"A purely azimuth passive localization model and adjustment scheme for UAV formation","authors":"Hao Sun, Linwei Dong, Xuhang Huang, Yuqi Fan, Yupeng Mei","doi":"10.1117/12.3001374","DOIUrl":"https://doi.org/10.1117/12.3001374","url":null,"abstract":"UAV clusters are a new type of cluster mode, which should maintain electromagnetic silence as much as possible during the formation flight. When the deviation of the UAVs position occurs during the flight, a pure direction finding method can be used to adjust the queue position of the UAVs. In this paper, we model and analyze the problem of position deviation for UAVs based on pure direction finding by optimizing theory and grid convenience method. Firstly, we establish an unbiased positioning model of the transmitter under ideal conditions. Then, we modify the model by equivalent conversion of deviation and establish the final biased positioning model of the transmitter. Finally, we simulate the actual UAV positioning situation through MATLAB simulation and verify the feasibility of our model.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"411 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116518472","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：