Single-Frame Difference-Based Image Fusion Glare-Resistant Detection System in Green Energy Vehicles
Green energy vehicles often use technologies that reduce lower inherent noise. However, adverse weather condition and low visibility at night can cause a glare effect from the headlights of oncoming cars. This poses a major threat to traffic safety. In order to solve this problem, this study initial...
Gespeichert in:
| Veröffentlicht in: | IEEE access 2024, Vol.12, p.110977-110991 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 110991 |
|---|---|
| container_issue | |
| container_start_page | 110977 |
| container_title | IEEE access |
| container_volume | 12 |
| creator | Lyu, Xiang Wang, Nan Gao, Jia |
| description | Green energy vehicles often use technologies that reduce lower inherent noise. However, adverse weather condition and low visibility at night can cause a glare effect from the headlights of oncoming cars. This poses a major threat to traffic safety. In order to solve this problem, this study initially adopts single frame difference for video frame selection, which reduces the computational load of image processing pipeline. Then, combined with visible and infrared images, this paper uses non-downsampled contourlet transform to achieve glare elimination. Finally, an improved convolutional network is used to detect pedestrians in anti-glare images, and volumetric Kalman filter algorithm is used to track pedestrians. Through these operations, the research establishes a Single-Frame Difference-Based Image Fusion Glare-Resistant Detection System applicable to green energy vehicles. The experimental analysis shows that the designed system can eliminate glare more than 80%, and the pedestrian detection accuracy reaches 95.44%. The constructed system aids green energy vehicles in accurately perceiving their surroundings during nighttime driving, ensuring safe travel. |
| doi_str_mv | 10.1109/ACCESS.2024.3424812 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_ieee_primary_10589393</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10589393</ieee_id><doaj_id>oai_doaj_org_article_59f842d7f3974563b696fb227b9ba8b9</doaj_id><sourcerecordid>3096239336</sourcerecordid><originalsourceid>FETCH-LOGICAL-c244t-4a60cc9bb35eab7db7f09b56ca4b285685605b9842aaa300dab034df11c9cc203</originalsourceid><addsrcrecordid>eNpNkV9LwzAUxYsoKOon0IeCz51p_rV51LnNgSA49TXcpDczY2s16R727U2tiOFCwr3n_HLhZNlVSSZlSdTt3XQ6W60mlFA-YZzyuqRH2RktpSqYYPL43_s0u4xxQ9KpU0tUZ1mz8u16i8U8wA7zB-8cBmwtFvcQscmXO1hjPt9H37X5YgsBixeMPvbQ9vkD9mj7YbI6xB53uU-agNjmsxbD-pC_44e3W4wX2YmDbcTL3_s8e5vPXqePxdPzYjm9eyos5bwvOEhirTKGCQRTNaZyRBkhLXBDayFTEWFUzSkAMEIaMITxxpWlVdZSws6z5chtOtjoz-B3EA66A69_Gl1Yawj9sJIWyiVOUzmmKi4kM1JJZyitjDJQG5VYNyPrM3Rfe4y93nT70Kb1NSNKUqYYk0nFRpUNXYwB3d-vJdFDOnpMRw_p6N90kut6dHlE_OcQtRqw33BoitE</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3096239336</pqid></control><display><type>article</type><title>Single-Frame Difference-Based Image Fusion Glare-Resistant Detection System in Green Energy Vehicles</title><source>Directory of Open Access Journals</source><source>IEEE Xplore Open Access Journals</source><source>EZB Electronic Journals Library</source><creator>Lyu, Xiang ; Wang, Nan ; Gao, Jia</creator><creatorcontrib>Lyu, Xiang ; Wang, Nan ; Gao, Jia</creatorcontrib><description>Green energy vehicles often use technologies that reduce lower inherent noise. However, adverse weather condition and low visibility at night can cause a glare effect from the headlights of oncoming cars. This poses a major threat to traffic safety. In order to solve this problem, this study initially adopts single frame difference for video frame selection, which reduces the computational load of image processing pipeline. Then, combined with visible and infrared images, this paper uses non-downsampled contourlet transform to achieve glare elimination. Finally, an improved convolutional network is used to detect pedestrians in anti-glare images, and volumetric Kalman filter algorithm is used to track pedestrians. Through these operations, the research establishes a Single-Frame Difference-Based Image Fusion Glare-Resistant Detection System applicable to green energy vehicles. The experimental analysis shows that the designed system can eliminate glare more than 80%, and the pedestrian detection accuracy reaches 95.44%. The constructed system aids green energy vehicles in accurately perceiving their surroundings during nighttime driving, ensuring safe travel.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2024.3424812</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Accuracy ; Algorithms ; Alternative energy ; Automobiles ; Clean energy ; Computer vision ; Detection algorithms ; Glare ; glare-free ; Green energy ; Green energy vehicles ; Image color analysis ; Image filters ; Image fusion ; Image processing ; Infrared imagery ; Infrared tracking ; Kalman filters ; Low visibility ; Night vision ; Nighttime construction ; pedestrian detection ; Pedestrians ; Renewable energy ; Safety ; single-frame difference ; Weather</subject><ispartof>IEEE access, 2024, Vol.12, p.110977-110991</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c244t-4a60cc9bb35eab7db7f09b56ca4b285685605b9842aaa300dab034df11c9cc203</cites><orcidid>0009-0006-4889-9678</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10589393$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>315,781,785,865,2103,4025,27638,27928,27929,27930,54938</link.rule.ids></links><search><creatorcontrib>Lyu, Xiang</creatorcontrib><creatorcontrib>Wang, Nan</creatorcontrib><creatorcontrib>Gao, Jia</creatorcontrib><title>Single-Frame Difference-Based Image Fusion Glare-Resistant Detection System in Green Energy Vehicles</title><title>IEEE access</title><addtitle>Access</addtitle><description>Green energy vehicles often use technologies that reduce lower inherent noise. However, adverse weather condition and low visibility at night can cause a glare effect from the headlights of oncoming cars. This poses a major threat to traffic safety. In order to solve this problem, this study initially adopts single frame difference for video frame selection, which reduces the computational load of image processing pipeline. Then, combined with visible and infrared images, this paper uses non-downsampled contourlet transform to achieve glare elimination. Finally, an improved convolutional network is used to detect pedestrians in anti-glare images, and volumetric Kalman filter algorithm is used to track pedestrians. Through these operations, the research establishes a Single-Frame Difference-Based Image Fusion Glare-Resistant Detection System applicable to green energy vehicles. The experimental analysis shows that the designed system can eliminate glare more than 80%, and the pedestrian detection accuracy reaches 95.44%. The constructed system aids green energy vehicles in accurately perceiving their surroundings during nighttime driving, ensuring safe travel.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Alternative energy</subject><subject>Automobiles</subject><subject>Clean energy</subject><subject>Computer vision</subject><subject>Detection algorithms</subject><subject>Glare</subject><subject>glare-free</subject><subject>Green energy</subject><subject>Green energy vehicles</subject><subject>Image color analysis</subject><subject>Image filters</subject><subject>Image fusion</subject><subject>Image processing</subject><subject>Infrared imagery</subject><subject>Infrared tracking</subject><subject>Kalman filters</subject><subject>Low visibility</subject><subject>Night vision</subject><subject>Nighttime construction</subject><subject>pedestrian detection</subject><subject>Pedestrians</subject><subject>Renewable energy</subject><subject>Safety</subject><subject>single-frame difference</subject><subject>Weather</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkV9LwzAUxYsoKOon0IeCz51p_rV51LnNgSA49TXcpDczY2s16R727U2tiOFCwr3n_HLhZNlVSSZlSdTt3XQ6W60mlFA-YZzyuqRH2RktpSqYYPL43_s0u4xxQ9KpU0tUZ1mz8u16i8U8wA7zB-8cBmwtFvcQscmXO1hjPt9H37X5YgsBixeMPvbQ9vkD9mj7YbI6xB53uU-agNjmsxbD-pC_44e3W4wX2YmDbcTL3_s8e5vPXqePxdPzYjm9eyos5bwvOEhirTKGCQRTNaZyRBkhLXBDayFTEWFUzSkAMEIaMITxxpWlVdZSws6z5chtOtjoz-B3EA66A69_Gl1Yawj9sJIWyiVOUzmmKi4kM1JJZyitjDJQG5VYNyPrM3Rfe4y93nT70Kb1NSNKUqYYk0nFRpUNXYwB3d-vJdFDOnpMRw_p6N90kut6dHlE_OcQtRqw33BoitE</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Lyu, Xiang</creator><creator>Wang, Nan</creator><creator>Gao, Jia</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0009-0006-4889-9678</orcidid></search><sort><creationdate>2024</creationdate><title>Single-Frame Difference-Based Image Fusion Glare-Resistant Detection System in Green Energy Vehicles</title><author>Lyu, Xiang ; Wang, Nan ; Gao, Jia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c244t-4a60cc9bb35eab7db7f09b56ca4b285685605b9842aaa300dab034df11c9cc203</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Alternative energy</topic><topic>Automobiles</topic><topic>Clean energy</topic><topic>Computer vision</topic><topic>Detection algorithms</topic><topic>Glare</topic><topic>glare-free</topic><topic>Green energy</topic><topic>Green energy vehicles</topic><topic>Image color analysis</topic><topic>Image filters</topic><topic>Image fusion</topic><topic>Image processing</topic><topic>Infrared imagery</topic><topic>Infrared tracking</topic><topic>Kalman filters</topic><topic>Low visibility</topic><topic>Night vision</topic><topic>Nighttime construction</topic><topic>pedestrian detection</topic><topic>Pedestrians</topic><topic>Renewable energy</topic><topic>Safety</topic><topic>single-frame difference</topic><topic>Weather</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lyu, Xiang</creatorcontrib><creatorcontrib>Wang, Nan</creatorcontrib><creatorcontrib>Gao, Jia</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Xplore Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) Online</collection><collection>IEEE Electronic Library Online</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lyu, Xiang</au><au>Wang, Nan</au><au>Gao, Jia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Single-Frame Difference-Based Image Fusion Glare-Resistant Detection System in Green Energy Vehicles</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2024</date><risdate>2024</risdate><volume>12</volume><spage>110977</spage><epage>110991</epage><pages>110977-110991</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>Green energy vehicles often use technologies that reduce lower inherent noise. However, adverse weather condition and low visibility at night can cause a glare effect from the headlights of oncoming cars. This poses a major threat to traffic safety. In order to solve this problem, this study initially adopts single frame difference for video frame selection, which reduces the computational load of image processing pipeline. Then, combined with visible and infrared images, this paper uses non-downsampled contourlet transform to achieve glare elimination. Finally, an improved convolutional network is used to detect pedestrians in anti-glare images, and volumetric Kalman filter algorithm is used to track pedestrians. Through these operations, the research establishes a Single-Frame Difference-Based Image Fusion Glare-Resistant Detection System applicable to green energy vehicles. The experimental analysis shows that the designed system can eliminate glare more than 80%, and the pedestrian detection accuracy reaches 95.44%. The constructed system aids green energy vehicles in accurately perceiving their surroundings during nighttime driving, ensuring safe travel.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2024.3424812</doi><tpages>15</tpages><orcidid>https://orcid.org/0009-0006-4889-9678</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2169-3536 |
| ispartof | IEEE access, 2024, Vol.12, p.110977-110991 |
| issn | 2169-3536 2169-3536 |
| language | eng |
| recordid | cdi_ieee_primary_10589393 |
| source | Directory of Open Access Journals; IEEE Xplore Open Access Journals; EZB Electronic Journals Library |
| subjects | Accuracy Algorithms Alternative energy Automobiles Clean energy Computer vision Detection algorithms Glare glare-free Green energy Green energy vehicles Image color analysis Image filters Image fusion Image processing Infrared imagery Infrared tracking Kalman filters Low visibility Night vision Nighttime construction pedestrian detection Pedestrians Renewable energy Safety single-frame difference Weather |
| title | Single-Frame Difference-Based Image Fusion Glare-Resistant Detection System in Green Energy Vehicles |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-13T17%3A46%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Single-Frame%20Difference-Based%20Image%20Fusion%20Glare-Resistant%20Detection%20System%20in%20Green%20Energy%20Vehicles&rft.jtitle=IEEE%20access&rft.au=Lyu,%20Xiang&rft.date=2024&rft.volume=12&rft.spage=110977&rft.epage=110991&rft.pages=110977-110991&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2024.3424812&rft_dat=%3Cproquest_ieee_%3E3096239336%3C/proquest_ieee_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3096239336&rft_id=info:pmid/&rft_ieee_id=10589393&rft_doaj_id=oai_doaj_org_article_59f842d7f3974563b696fb227b9ba8b9&rfr_iscdi=true |