A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transf...A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform （RDWT） based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.展开更多
Aiming at solving the problem of missed detection and low accuracy in detecting traffic signs in the wild, an improved method of YOLOv8 is proposed. Firstly, combined with the characteristics of small target objects i...Aiming at solving the problem of missed detection and low accuracy in detecting traffic signs in the wild, an improved method of YOLOv8 is proposed. Firstly, combined with the characteristics of small target objects in the actual scene, this paper further adds blur and noise operation. Then, the asymptotic feature pyramid network (AFPN) is introduced to highlight the influence of key layer features after feature fusion, and simultaneously solve the direct interaction of non-adjacent layers. Experimental results on the TT100K dataset show that compared with the YOLOv8, the detection accuracy and recall are higher. .展开更多
In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movemen...In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movements is presented. This method has an adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes.The 'urgency degree' term, which can describe the different user's demand for green time is used in decision-making by which strategy of signal timing can be determined. Using a fuzzy logic controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. In this paper, a method based on fuzzy-neuro can be used to predict traffic parameters used in fuzzy logic controller. The feasibility of using a multi-objective genetic algorithm ( MOGA) to find a group of optimizing sets of parameters for fuzzy logic controller depending on different objects is also demonstrated. Simulation results show that the proposed methed is effecfive to adjust the signal timing in response to changing traffic conditions on a real-time basis, and the controller can produce lower vehicle delays and percentage of stopped vehicles than a traffic-actuated controller.展开更多
Under the demand of strategic air traffic flow management and the concept of trajectory based operations（TBO）,the network-wide 4D flight trajectories planning（N4DFTP） problem has been investigated with the purpose...Under the demand of strategic air traffic flow management and the concept of trajectory based operations（TBO）,the network-wide 4D flight trajectories planning（N4DFTP） problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories（4DTs）（3D position and time） for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategiclevel conflict management is developed in this paper.Specifically,a bi-objective N4 DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm（MOMMA） that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4 DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.展开更多
尽管基于深度学习的目标检测在交通场景的应用已经取得了一定的进展,但复杂交通场景多目标精度与速度的博弈仍然是个挑战。大多数提升精度的方法都是参数密集型的,大大增加了模型的参数量。针对这一难题,提出了基于YOLOv8的稀疏参数模型...尽管基于深度学习的目标检测在交通场景的应用已经取得了一定的进展,但复杂交通场景多目标精度与速度的博弈仍然是个挑战。大多数提升精度的方法都是参数密集型的,大大增加了模型的参数量。针对这一难题,提出了基于YOLOv8的稀疏参数模型,实现在降低参数量的同时提升模型的召回率和检测精度。首先使用简单注意力机制(Simple Attention Mechanism,SimAM)以建立更强劲的骨干网络提取特征;其次提出轻量化的内容感知特征重组模块(Lightweight Content-Aware ReAssembly of Features,L-CARAFE)代替上采样操作,在一个更大的感受野上聚合上下文信息;最后通过稀疏参数的多解耦头,在降低参数量的同时提升模型的检测精度。由于交通场景的复杂性,不仅通过KITTI数据集验证模型的有效性,还通过COCO数据集验证模型的泛化性。该模型在公开的数据集上均能大幅提升召回率和平均精度(mean Average Precision,mAP),其中,nano在KITTI数据集上以2.95的参数量使召回率和mAP分别提高了3.1%和0.9%,小模型在COCO数据集上的mAP@0.5达到60.6%。展开更多
为了综合分析YOLO(You Only Look Once)算法在提升交通安全性和效率方面的重要作用,从“人-车-路”3个核心要素的角度,对YOLO算法在交通目标检测中的发展和研究现状进行系统性地总结.概述了YOLO算法常用的评价指标,详细阐述了这些指标...为了综合分析YOLO(You Only Look Once)算法在提升交通安全性和效率方面的重要作用,从“人-车-路”3个核心要素的角度,对YOLO算法在交通目标检测中的发展和研究现状进行系统性地总结.概述了YOLO算法常用的评价指标,详细阐述了这些指标在交通场景中的实际意义.对YOLO算法的核心架构进行概述,追溯了该算法的发展历程,分析各个版本迭代中的优化和改进措施.从“人-车-路”3种交通目标的视角出发,梳理并论述了采用YOLO算法进行交通目标检测的研究现状及应用情况.分析目前YOLO算法在交通目标检测中存在的局限性和挑战,提出相应的改进方法,展望未来的研究重点,为道路交通的智能化发展提供了研究参考.展开更多
文摘A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform （RDWT） based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.
文摘Aiming at solving the problem of missed detection and low accuracy in detecting traffic signs in the wild, an improved method of YOLOv8 is proposed. Firstly, combined with the characteristics of small target objects in the actual scene, this paper further adds blur and noise operation. Then, the asymptotic feature pyramid network (AFPN) is introduced to highlight the influence of key layer features after feature fusion, and simultaneously solve the direct interaction of non-adjacent layers. Experimental results on the TT100K dataset show that compared with the YOLOv8, the detection accuracy and recall are higher. .
基金This project was supported by China Postdoctoral Science Foundation: "Research on Traffic Signal Control Method for Urban Intersection Based on Intelligent Techniques, 2001" .
文摘In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movements is presented. This method has an adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes.The 'urgency degree' term, which can describe the different user's demand for green time is used in decision-making by which strategy of signal timing can be determined. Using a fuzzy logic controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. In this paper, a method based on fuzzy-neuro can be used to predict traffic parameters used in fuzzy logic controller. The feasibility of using a multi-objective genetic algorithm ( MOGA) to find a group of optimizing sets of parameters for fuzzy logic controller depending on different objects is also demonstrated. Simulation results show that the proposed methed is effecfive to adjust the signal timing in response to changing traffic conditions on a real-time basis, and the controller can produce lower vehicle delays and percentage of stopped vehicles than a traffic-actuated controller.
基金co-supported by the National Science Foundation for Young Scientists of China(No.61401011)the National Key Technologies R&D Program of China(No.2015BAG15B01)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.61521091)
文摘Under the demand of strategic air traffic flow management and the concept of trajectory based operations（TBO）,the network-wide 4D flight trajectories planning（N4DFTP） problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories（4DTs）（3D position and time） for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategiclevel conflict management is developed in this paper.Specifically,a bi-objective N4 DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm（MOMMA） that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4 DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.
文摘尽管基于深度学习的目标检测在交通场景的应用已经取得了一定的进展,但复杂交通场景多目标精度与速度的博弈仍然是个挑战。大多数提升精度的方法都是参数密集型的,大大增加了模型的参数量。针对这一难题,提出了基于YOLOv8的稀疏参数模型,实现在降低参数量的同时提升模型的召回率和检测精度。首先使用简单注意力机制(Simple Attention Mechanism,SimAM)以建立更强劲的骨干网络提取特征;其次提出轻量化的内容感知特征重组模块(Lightweight Content-Aware ReAssembly of Features,L-CARAFE)代替上采样操作,在一个更大的感受野上聚合上下文信息;最后通过稀疏参数的多解耦头,在降低参数量的同时提升模型的检测精度。由于交通场景的复杂性,不仅通过KITTI数据集验证模型的有效性,还通过COCO数据集验证模型的泛化性。该模型在公开的数据集上均能大幅提升召回率和平均精度(mean Average Precision,mAP),其中,nano在KITTI数据集上以2.95的参数量使召回率和mAP分别提高了3.1%和0.9%,小模型在COCO数据集上的mAP@0.5达到60.6%。
文摘为了综合分析YOLO(You Only Look Once)算法在提升交通安全性和效率方面的重要作用,从“人-车-路”3个核心要素的角度,对YOLO算法在交通目标检测中的发展和研究现状进行系统性地总结.概述了YOLO算法常用的评价指标,详细阐述了这些指标在交通场景中的实际意义.对YOLO算法的核心架构进行概述,追溯了该算法的发展历程,分析各个版本迭代中的优化和改进措施.从“人-车-路”3种交通目标的视角出发,梳理并论述了采用YOLO算法进行交通目标检测的研究现状及应用情况.分析目前YOLO算法在交通目标检测中存在的局限性和挑战,提出相应的改进方法,展望未来的研究重点,为道路交通的智能化发展提供了研究参考.
