Mr. Yongsheng Lv Profile

Yongsheng Lv

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Proceedings Article | 13 December 2024 Paper

Categorized visible-to-infrared image generation based on physical properties of different targets

Xiaolu Zhan, Shaozhe Cui, Guojin Feng, Zuo Chen, Wende Liu, Yongsheng Lv, Haiyong Gan

Proceedings Volume 13493, 134930F (2024) https://doi.org/10.1117/12.3048166

KEYWORDS: Data modeling, Infrared radiation, Infrared imaging, Object detection, Image quality, Target detection, Thermal modeling

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The mapping relationship between visible images and infrared images of different targets varies due to differences in their physical properties, such as surface emissivity, reflectivity, temperature, ambient radiation intensity, and heat dissipation effects. When using Generative Adversarial Networks for visible-to-infrared image translation, we focus not only on the overall quality of the image but also on the accuracy of target feature translation. In previous research, the authors used the AVIID-3 dataset to classify cars targets into four categories: light-colored moving cars A, dark-colored moving cars B, light-colored parked cars C, and dark-colored parked cars D. The scenes are divided into two categories: moving scenes with only moving car targets and parking lot scenes with only parked car targets. An optimal generation strategy for the AVIID-3 dataset based on Pix2pix and CycleGAN has been proposed. Although this research has made significant progress, some limitations still exist. In the AVIID-3 dataset, moving and parked car targets do not appear simultaneously in the same scene, making it impossible to evaluate different generation strategies in complex scenes. To address this issue, this study proposes a general visible-to-infrared image generation strategy for car targets. Additionally, data from complex scenes captured by drones were used to reconstruct the dataset. This approach validates that the proposed strategy is effective not only for simple scenes containing only one type of target but also for mixed scenes with random combinations of multiple targets, demonstrating its practical applicability in real engineering scenarios.

Proceedings Article | 4 December 2024 Paper

Evaluation of the generated visible-to-infrared images using CycleGAN and Pix2Pix based on pre-sorted AVIID-3 dataset

Xiaolu Zhan, Shaozhe Cui, Guojin Feng, Qi Zang, Zuo Chen, Wende Liu, Yongsheng Lv, Haiyong Gan

Proceedings Volume 13283, 132833C (2024) https://doi.org/10.1117/12.3037099

KEYWORDS: Infrared radiation, Infrared imaging, Target detection, Object detection, Infrared detectors, Visible radiation, Thermal modeling

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The infrared cameras capture images based on the infrared radiation energy. In fact, the sensor's reception of this radiation is affected by various factors, such as the emissivity and reflectivity of the target surface, the temperatures of both the environment and the target, the radiation intensity of the environment, and the heat dissipation effects. When using GANs (Generative Adversarial Networks) for visible-to-infrared image generation for different targets, it is not possible to analyze the target differences in feature transformation for different targets using overall image quality evaluation metrics, such as MSE (Mean Squared Error). This paper focuses on four typical targets from the AVIID-3 dataset and employs the rotational object detection network YOLOv8 OBB (YOLOv8 Oriented Bounding Boxes) as an evaluation tool. By comparing the absolute values of the mAP (Mean Average Precision) differences in YOLOv8 OBB detection between the CycleGAN and Pix2Pix generated visible-to-infrared targets and the real infrared targets, we evaluate the visible-to-infrared generation effects for the four types of targets. Additionally, this paper proposes an optimal generation strategy for the four typical targets without altering the network structure.

Proceedings Article | 23 January 2024 Paper

A multidimensional feature fusion method for unsupervised hyperspectral information extraction

Xinxin Gao, Yanheng Wang, Yongsheng Lv, Jianjun Sha, Xichuan Zheng

Proceedings Volume 12978, 129782X (2024) https://doi.org/10.1117/12.3019413

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The unsupervised algorithm extracts hyperspectral image features, focusing on one aspect and often ignoring some other information. To guarantee the effective extraction of information before image analysis, an unsupervised feature extraction technique with hyperspectral imaging data based on multidimensional feature fusion is proposed. First, we use principal component analysis (PCA) to map the high-dimensional data to a simpler space and extract the spectral features based on the elimination of redundant relationships. Then, the multi-directional spatial feature extraction algorithm of Gabor texture and morphology is utilized to extract each primary component's spatial properties. Finally, the spectral features, morphological features, and Gabor texture features are fused together by the vector stacking fusion. In this paper, the HSI information is extracted unsupervised using the previous technique, and HSI classification experiments using support vector machines are carried out to validate the efficacy of the information. The experiments demonstrate that the proposed method improves the Kappa coefficient by at least 14% in the MUUFL dataset and by 30% in the Trento dataset compared with the traditional spectral feature extraction, which significantly improves the classification accuracy.

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