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However, due to poor spatial resolution of LWIR cameras, thermal imaging provides limited textural information within a scene and hence may fail to provide adequate discriminatory information to identify between objects of similar texture, shape and size. To improve the object detection task in fog and occlusion, we use three-dimensional (3D) integral imaging (InIm) system with a visible range camera. 3D InIm provides depth information, mitigates the occlusion and fog in front of the object, and improves the object recognition capabilities. For object recognition, the YOLOv3 neural network is used for each of the tested imaging systems. Since the concentration of fog affects the images from different sensors (visible, LWIR, and Azure Kinect depth cameras) in different ways, we compared the performance of the network on these images in terms of average precision and average miss rate. For the experiments we conducted, the results indicate that in degraded environment 3D InIm using visible range cameras can provide better image reconstruction as compared to the LWIR camera and Azure Kinect RGBD camera, and therefore it may improve the detection accuracy of the network. 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The output of the generator portion of the network is projected onto a fully simulated virtual scenario. Instead of directly addressing the domain gap induced by this method, we show indirectly that training on the generated dataset improves the performance and robustness of 3D object detection algorithms.<\/jats:p>","DOI":"10.1007\/s12239-025-00319-4","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T02:09:38Z","timestamp":1760666978000},"page":"883-895","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Simulation of LiDAR Under Fog with Generative Adversarial Networks for Robust 3D Object Detection"],"prefix":"10.1007","volume":"27","author":[{"given":"Yeonsoo","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoonho","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jisub","family":"Kwak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yejin","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaewan","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"319_CR1","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1007\/s38314-023-1462-8","volume":"18","author":"N Ahn","year":"2023","unstructured":"Ahn, N., Cichy, Y., Zeh, T., & Haider, A. 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This study examines weather detectability using a lidar ceilometer by making an unprecedented attempt at detecting weather phenomena through the application of machine learning techniques to the backscatter data obtained from a lidar ceilometer. This study investigates the weather phenomena of precipitation and fog, which are expected to greatly affect backscatter data. In this experiment, the backscatter data obtained from the lidar ceilometer, CL51, installed in Boseong, South Korea, were used. For validation, the data from the automatic weather station for precipitation and visibility sensor PWD20 for fog, installed at the same location, were used. The experimental results showed potential for precipitation detection, which yielded an F1 score of 0.34. However, fog detection was found to be very difficult and yielded an F1 score of 0.10.<\/jats:p>","DOI":"10.3390\/app10186452","type":"journal-article","created":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T10:30:12Z","timestamp":1600252212000},"page":"6452","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0492-0889","authenticated-orcid":false,"given":"Yong-Hyuk","family":"Kim","sequence":"first","affiliation":[{"name":"School of Software, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3922-3995","authenticated-orcid":false,"given":"Seung-Hyun","family":"Moon","sequence":"additional","affiliation":[{"name":"School of Software, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yourim","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do 13120, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,16]]},"reference":[{"key":"ref_1","first-page":"364","article-title":"New optical concept for commercial lidar ceilometers scanning the boundary layer, Remote Sensing","volume":"5571","year":"2004","journal-title":"Int. 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However, existing physics-based augmentation models typically rely on single scattering approximations to predict light propagation under unfavorable conditions, such as fog. This can prevent the reproduction of important signal characteristics encountered in a real-world environment. Consequently, in this work, Monte Carlo simulations are employed to assess the relevance of multiple-scattered light to the detected LiDAR signal in different types of fog, with scattering phase functions calculated from Mie theory considering real particle size distributions. Bidirectional path tracing is used within the self-developed GPU-accelerated Monte Carlo software to compensate for the unfavorable photon statistics associated with the limited detection aperture of the LiDAR geometry. To validate the Monte Carlo software, an analytical solution of the radiative transfer equation for the time-resolved radiance in terms of scattering orders is derived, thereby providing an explicit representation of the double-scattered contributions. The results of the simulations demonstrate that the shape of the detected signal can be significantly impacted by multiple-scattered light, depending on LiDAR geometry and visibility. In particular, double-scattered light can dominate the overall signal at low visibilities. 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However, under bad weather conditions, such as rain, snow, and fog, LiDAR-detection performance is reduced. This effect has hardly been verified in actual road environments. In this study, tests were conducted with different precipitation levels (10, 20, 30, and 40 mm\/h) and fog visibilities (50, 100, and 150 m) on actual roads. Square test objects (60 \u00d7 60 cm2) made of retroreflective film, aluminum, steel, black sheet, and plastic, commonly used in Korean road traffic signs, were investigated. Number of point clouds (NPC) and intensity (reflection value of points) were selected as LiDAR performance indicators. These indicators decreased with deteriorating weather in order of light rain (10\u201320 mm\/h), weak fog (&lt;150 m), intense rain (30\u201340 mm\/h), and thick fog (\u226450 m). Retroreflective film preserved at least 74% of the NPC under clear conditions with intense rain (30\u201340 mm\/h) and thick fog (&lt;50 m). Aluminum and steel showed non-observation for distances of 20\u201330 m under these conditions. ANOVA and post hoc tests suggested that these performance reductions were statistically significant. Such empirical tests should clarify the LiDAR performance degradation.<\/jats:p>","DOI":"10.3390\/s23062972","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T02:05:54Z","timestamp":1678413954000},"page":"2972","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["Empirical Analysis of Autonomous Vehicle\u2019s LiDAR Detection Performance Degradation for Actual Road Driving in Rain and Fog"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3342-7935","authenticated-orcid":false,"given":"Jiyoon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2216-4917","authenticated-orcid":false,"given":"Bum-jin","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jisoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"ref_1","unstructured":"Korea Institute of Civil Engineering and Building Technology (2021). 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