899d4b2b40352001b884ab604ff8d336 sensors_22_03193_v3.pdf dcd39544b169a88d2641b631a0602f6d16928895 sensors_22_03193_v3.pdf 18ab05b39cf28f5563f07555751fea67274b0b502a7407140957ae0cd17372a2 sensors_22_03193_v3.pdf Title: Weather Classification by Utilizing Synthetic Data Subject: Weather prediction from real-world images can be termed a complex task when blacktargeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. blackIn this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, blackthe performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by blackas much as 74%. blackThe article paves a way forward to tackle the persistent problem of bias in vision-based datasets. Keywords: weather classification; synthetic data; dataset; autonomous car; computer vision; advanced driver assistance systems; deep learning; intelligent transportation systems Author: Saad Minhas, Zeba Khanam, Shoaib Ehsan, Aura Hernández-Sabaté and Klaus McDonald-Maier Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Wed Apr 27 07:51:59 2022 CEST ModDate: Wed Apr 27 08:34:59 2022 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 12 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 4131577 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- HHVHED+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 JMELCV+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 FVJDMR+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 27 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes no 31 0 MCBCCO+SourceSansRoman-Bold Type 1C Custom yes yes no 35 0 RVSEMT+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 47 0 NJAOJC+CMSY10 Type 1 Builtin yes yes no 76 0 DUYRLK+NimbusRomNo9L-Medi Type 1 Custom yes yes no 99 0 LBCCAA+NimbusRomNo9L-Regu Type 1 Custom yes yes no 105 0 WFNECM+NimbusRomNo9L-ReguItal Type 1 Custom yes yes no 111 0 NBAIUF+NimbusSanL-Regu Type 1 Custom yes yes no 117 0 OCKIWP+NimbusRomNo9L-MediItal Type 1 Custom yes yes no 123 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2023-02-28 02:39:55 CET RepresentationInformation: sensors_22_03193_v3.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2023-02-22 01:34:21 CET Size: 4131577 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 270 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Children: Item: Title: Related Work Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Item: Title: Contributions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: The Simulator Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: The Dataset Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Weather Classification Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Info: Title: Weather Classification by Utilizing Synthetic Data Author: Saad Minhas, Zeba Khanam, Shoaib Ehsan, Aura Hernández-Sabaté and Klaus McDonald-Maier Subject: Weather prediction from real-world images can be termed a complex task when blacktargeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. blackIn this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, blackthe performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by blackas much as 74%. blackThe article paves a way forward to tackle the persistent problem of bias in vision-based datasets. 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