Binocular Camera Calibration Based on BP Neural Network Optimized by Improved Particle Swarm Optimization

Zhang, Yueyi; Wang, Xiaoyi; Jiang, Hesong; Wang, Xueyuan

doi:10.1109/prai55851.2022.9904040

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…BPNN is a typical multilayer feedforward neural network trained using an error backpropagation algorithm for learning containing input, hidden, and output layers 35 – 37 The topology of the BPNN is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Comparative evaluation of backpropagation neural network and genetic algorithm-backpropagation neural network models for PM2.5 concentration prediction based on aerosol optical depth, meteorological factors, and air pollutants

Gu,

Liang,

Song

et al. 2023

J. Appl. Rem. Sens.

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Fine particles with an aerodynamic diameter ≤2.5 μm are called PM 2.5 , and accurate prediction of PM 2.5 concentration can help prevent the harmful effects of heavy pollution on humans. At present, the distribution of ground-based PM 2.5 monitoring stations in China's cities is relatively sparse. Hence, the aerosol optical depth (AOD) obtained from satellite remote sensing provides an effective means for large-scale routine PM 2.5 monitoring. In this study, the multi-angle implementation of atmospheric correction AOD (1 km resolution) product from the moderate resolution imaging spectroradiometer (MODIS), meteorological factors, and ground measurements of daily PM 2.5 concentrations from 2016 to 2020 for Dalian were input into a backpropagation neural network (BPNN) to predict PM 2.5 concentrations. To improve the prediction accuracy and stability, a genetic algorithm (GA)-optimized BPNN was further established based on the BPNN to achieve a comparative PM 2.5 concentration prediction. Results showed that the BPNN and GA-BPNN achieved the PM 2.5 concentration by integrating AOD, meteorological factors, and air pollutants with the model test set R 2 of 0.77 and 0.83 and root mean square error (RMSE) of 11.83 and 9.80 μg m −3 , respectively. GA-BPNN decreased the SD RMSE from 0.44 to 0.23 μg m −3 compared with BPNN and improved the model stability. The spatial distribution of annual averaged estimated PM 2.5 was predicted using GA-BPNN in Dalian from 2016 to 2020. The spatial distribution of PM 2.5 concentrations was generally consistent over 5 years, and the PM 2.5 concentrations exhibited an overall decreasing trend. The BPNN before and after optimization achieved longer-term interannual PM 2.5 daily concentration prediction, and the GA-BPNN had a better prediction effect for extreme values, handled complex fuzzy mapping relationships, and had lower computational complexity. Hence, GA-BPNN was found to be more suitable for practical applications with more advantages for PM 2.5 concentration prediction than BPNN.

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Section: Methodsmentioning

confidence: 99%

Comparative evaluation of backpropagation neural network and genetic algorithm-backpropagation neural network models for PM2.5 concentration prediction based on aerosol optical depth, meteorological factors, and air pollutants

Gu,

Liang,

Song

et al. 2023

J. Appl. Rem. Sens.

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show abstract

“…If the initial estimate is not close to the optimal values, the optimization process may become unstable or get trapped in a local minimum. To mitigate this issue, intelligent global optimization algorithms, such as GA [ 25 , 26 ] and PSO [ 27 , 28 ], have been applied to achieve accurate camera calibration. While these methods have been successfully applied in other research fields, such as electromagnetics [ 29 , 30 , 31 ] and medicine [ 32 ], they have also shown promise in the area of camera calibration.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization

Li,

Yoon

2024

Sensors

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Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.

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“…The first step of camera calibration is to use visible light and infrared camera to take two images of the target on the same calibration board, that is, visible light image and infrared image [4]. The two images are preprocessed, including graying and denoising.…”

Section: Visible and Infrared Image Acquisition And Preprocessingmentioning

confidence: 99%

Calibration method of visible light and infrared camera based on edge detection

Zou¹,

Mo²,

Wang³

et al. 2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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Visible and infrared cameras can solve the practical problem of high-resolution image recognition in complex operation scenes of substations, but the prerequisite is that visible and infrared cameras need to be calibrated effectively. In this paper, a calibration method of visible light and infrared camera based on edge detection is proposed. The method adopts a visible light camera and an infrared camera to shoot two images of the same calibration plate, namely a visible light image and an infrared image, and implements preprocessing. It detects the edges of two images, extracts feature point description information by using a scale invariant feature conversion algorithm and determines four groups of determined matching point pairs. It solves a transformation model by combining an internal parameter matrix to obtain an external parameter matrix and realizes visible light and infrared camera calibration. The results verify that the parallel equipotential error is less than zero. One under the application of the method, which indicates that the calibration effect of the method is better.

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Binocular Camera Calibration Based on BP Neural Network Optimized by Improved Particle Swarm Optimization

Cited by 4 publications

References 6 publications

Comparative evaluation of backpropagation neural network and genetic algorithm-backpropagation neural network models for PM2.5 concentration prediction based on aerosol optical depth, meteorological factors, and air pollutants

Comparative evaluation of backpropagation neural network and genetic algorithm-backpropagation neural network models for PM2.5 concentration prediction based on aerosol optical depth, meteorological factors, and air pollutants

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization

Calibration method of visible light and infrared camera based on edge detection

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