The YOLO-based Multi-Pulse Lidar (YMPL) for target detection in hazy weather
•YMPL can accurately detect targets in fog and expand the detection range.•New data preprocessing for creating bitmaps from multiple pulses.•The data preprocessing can clearly demonstrate the position of the target in the fog.•The YOLO and preprocessing combo improve target recognition in harsh weat...
Uloženo v:
| Vydáno v: | Optics and lasers in engineering Ročník 177; s. 108131 |
|---|---|
| Hlavní autoři: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.06.2024
|
| Témata: | |
| ISSN: | 0143-8166, 1873-0302 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | •YMPL can accurately detect targets in fog and expand the detection range.•New data preprocessing for creating bitmaps from multiple pulses.•The data preprocessing can clearly demonstrate the position of the target in the fog.•The YOLO and preprocessing combo improve target recognition in harsh weather.
As one of the essential sensing technologies for autonomous driving, Lidar has not been widely adopted due to the significant impact of foggy and hazy weather leading to inaccurate target detection and distance measurement. In this paper, a YOLO-based Multi-Pulse Lidar system (YMPL) is proposed for accurate target detection in foggy conditions. The system integrates multiple one-dimensional pulse detection courses into a two-dimensional image and utilizes the YOLO target recognition algorithm to identify real target echoes and measure the distance of the target. The simulation and experimental results demonstrate that the YMPL system effectively mitigates the interference of fog and noise on pulse detection. Thereby the detection probability improves and the detection range extends. The system also shows the excellent anti-jitter ability. Under the circumstance of a 40 % backscattering coefficient, the system achieves a mean absolute error (MAE) of only 0.013 m within the range of 45.5 m, significantly outperforming the traditional threshold detection and ResNet, SVD-CNN and VIT algorithm. This lays a solid foundation for the all-weather practical application of lidar. |
|---|---|
| AbstractList | •YMPL can accurately detect targets in fog and expand the detection range.•New data preprocessing for creating bitmaps from multiple pulses.•The data preprocessing can clearly demonstrate the position of the target in the fog.•The YOLO and preprocessing combo improve target recognition in harsh weather.
As one of the essential sensing technologies for autonomous driving, Lidar has not been widely adopted due to the significant impact of foggy and hazy weather leading to inaccurate target detection and distance measurement. In this paper, a YOLO-based Multi-Pulse Lidar system (YMPL) is proposed for accurate target detection in foggy conditions. The system integrates multiple one-dimensional pulse detection courses into a two-dimensional image and utilizes the YOLO target recognition algorithm to identify real target echoes and measure the distance of the target. The simulation and experimental results demonstrate that the YMPL system effectively mitigates the interference of fog and noise on pulse detection. Thereby the detection probability improves and the detection range extends. The system also shows the excellent anti-jitter ability. Under the circumstance of a 40 % backscattering coefficient, the system achieves a mean absolute error (MAE) of only 0.013 m within the range of 45.5 m, significantly outperforming the traditional threshold detection and ResNet, SVD-CNN and VIT algorithm. This lays a solid foundation for the all-weather practical application of lidar. |
| ArticleNumber | 108131 |
| Author | Wu, Long Xu, Lu Yang, Chenghua Zhang, Wei Yang, Xu Gong, Fuxiang Chen, Shuyu Zhang, Jianlong Zhang, Yong |
| Author_xml | – sequence: 1 givenname: Long surname: Wu fullname: Wu, Long organization: School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China – sequence: 2 givenname: Fuxiang surname: Gong fullname: Gong, Fuxiang organization: School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China – sequence: 3 givenname: Xu surname: Yang fullname: Yang, Xu email: yangxu@zstu.edu.cn organization: School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China – sequence: 4 givenname: Lu surname: Xu fullname: Xu, Lu organization: School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China – sequence: 5 givenname: Shuyu surname: Chen fullname: Chen, Shuyu organization: Keyi College of Zhejiang Sci-Tech University, Shaoxing 312369, China – sequence: 6 givenname: Yong surname: Zhang fullname: Zhang, Yong organization: Institute of Optical Target Simulation and Test Technology, Harbin Institute of Technology, Harbin 150001, China – sequence: 7 givenname: Jianlong surname: Zhang fullname: Zhang, Jianlong organization: Institute of Optical Target Simulation and Test Technology, Harbin Institute of Technology, Harbin 150001, China – sequence: 8 givenname: Chenghua surname: Yang fullname: Yang, Chenghua organization: Beijing Institute of Remote Sensing Equipment, Beijing 110000, China – sequence: 9 givenname: Wei surname: Zhang fullname: Zhang, Wei organization: Beijing Institute of Remote Sensing Equipment, Beijing 110000, China |
| BookMark | eNqNkD1PwzAQQC1UJNrCb8AjDCm-2ImTgaGq-JJStUMZOlmufWldhaRyXFD59aQqYmCB6aST3tPdG5Be3dRIyDWwETBI77ajZhcq3WK9HsUsFt02Aw5npA-Z5BHjLO6RPgPBowzS9IIM2nbLOlIA9Emx2CBdzopZtOoUlk73VXDRfF-1SAtntac3y-m8uKVl42nQfo2BWgxogmtq6mq60Z8H-oE6bNBfkvNSd-TV9xyS18eHxeQ5KmZPL5NxERkOSYhWmWbGWpHEqzjBlCeY8zK3ZY6Ci9xaKcHGeRYjSMFkxgVIK9Dm3EiL3CR8SO5PXuObtvVYKuOCPl4UvHaVAqaOadRW_aRRxzTqlKbj5S9-592b9od_kOMTid177w69ao3D2qB1vmuibOP-dHwB5GaEqg |
| CitedBy_id | crossref_primary_10_1016_j_infrared_2025_105956 crossref_primary_10_1016_j_optlastec_2025_113951 crossref_primary_10_1364_AO_570214 crossref_primary_10_1016_j_measurement_2025_117875 crossref_primary_10_1038_s41598_025_92112_7 crossref_primary_10_1016_j_infrared_2024_105639 crossref_primary_10_1016_j_aej_2025_04_105 |
| Cites_doi | 10.3390/rs14194960 10.3390/fractalfract2010008 10.1016/j.infrared.2023.104613 10.1080/15481603.2023.2227394 10.1364/OL.487477 10.1109/JPHOT.2022.3185304 10.1016/j.measurement.2021.110313 10.1016/j.optlastec.2023.109807 10.1109/TAP.2022.3172759 10.1109/TPAMI.2022.3152247 10.1016/j.optlastec.2022.108749 10.1109/MVT.2019.2892497 10.1109/LRA.2020.2972865 10.1109/18.382009 10.3390/app13063772 10.1016/j.optlaseng.2023.107658 10.1002/spe.2325 10.1109/LSENS.2020.3018708 10.3390/sym11080997 10.1109/LRA.2023.3311371 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier Ltd |
| Copyright_xml | – notice: 2024 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.optlaseng.2024.108131 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Physics |
| EISSN | 1873-0302 |
| ExternalDocumentID | 10_1016_j_optlaseng_2024_108131 S0143816624001106 |
| GroupedDBID | --K --M .~1 0R~ 123 1B1 1RT 1~. 1~5 29N 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN AABXZ AACTN AAEDT AAEDW AAEPC AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO ABFNM ABJNI ABMAC ABNEU ABXDB ABXRA ABYKQ ACDAQ ACFVG ACGFS ACNNM ACRLP ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AEZYN AFKWA AFRZQ AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIKHN AITUG AIVDX AJBFU AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BBWZM BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q G8K GBLVA HMV HVGLF HZ~ IHE J1W JJJVA KOM LY7 M38 M41 MAGPM MO0 N9A NDZJH O-L O9- OAUVE OGIMB OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ SDF SDG SDP SES SET SEW SPC SPCBC SPD SPG SSM SSQ SST SSZ T5K VOH WUQ XPP ZMT ~02 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c315t-b8a0cdd452b25e635e93f9df9e4349dd771d2982e1740783417d4ed93c7de3c53 |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001199823400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0143-8166 |
| IngestDate | Sat Nov 29 07:27:52 EST 2025 Tue Nov 18 22:41:42 EST 2025 Sat Mar 30 16:18:29 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Lidar Foggy detection YOLO target recognition algorithm |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c315t-b8a0cdd452b25e635e93f9df9e4349dd771d2982e1740783417d4ed93c7de3c53 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_optlaseng_2024_108131 crossref_primary_10_1016_j_optlaseng_2024_108131 elsevier_sciencedirect_doi_10_1016_j_optlaseng_2024_108131 |
| PublicationCentury | 2000 |
| PublicationDate | June 2024 2024-06-00 |
| PublicationDateYYYYMMDD | 2024-06-01 |
| PublicationDate_xml | – month: 06 year: 2024 text: June 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Optics and lasers in engineering |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Li, Pan, Shen (bib0022) 2023; 157 Zhang, Zhou, Wang (bib0024) 2019; 11 Sang, Tsai, Yu (bib0008) 2020; 4 Han, Wang, Chen (bib0027) 2022; 45 Wang, Liu, Yang (bib0007) 2023; 8 Wang, Liu, Jia (bib0011) 2020; 11567 Zou, Huang, Liu (bib0012) 2023; 167 Liu, Jin, Que (bib0004) 2023; 168 Casasanta, Garra (bib0016) 2018; 2 Luo, Liu, Hua (bib0028) 2021 Liu, Li, Chen (bib0001) 2023; 48 Piovan, Hodgson, Mozzi (bib0002) 2023; 60 Donoho (bib0010) 1995; 41 Wang, Li, Yang (bib0026) 2022; 70 Redmon, Divvala, Girshick (bib0021) 2016 Jiang, Zhu, Jiang (bib0023) 2023; 13 He, Zhang, Ren (bib0025) 2016 Mau, Trumpf, Day (bib0009) 2022; 12274 Zang, Ding, Smith (bib0005) 2019; 14 Dai, Zhao, Li (bib0019) 2022; 14 Xu, Wang, Wu (bib0017) 2023; 130 Hahner, Sakaridis, Dai (bib0018) 2021 Chambi, Lemire, Kaser (bib0020) 2016; 46 Robin, Florian, Philipp (bib0014) 2020; 5 Ren, Zhao, Liu (bib0003) 2022; 187 Szegedy, Vanhoucke, Ioffe (bib0013) 2016 Li, Li, Mao (bib0006) 2022; 14 Ren, Haishan, Huo (bib0015) 2015; 37 Li (10.1016/j.optlaseng.2024.108131_bib0006) 2022; 14 Hahner (10.1016/j.optlaseng.2024.108131_bib0018) 2021 He (10.1016/j.optlaseng.2024.108131_bib0025) 2016 Mau (10.1016/j.optlaseng.2024.108131_bib0009) 2022; 12274 Li (10.1016/j.optlaseng.2024.108131_bib0022) 2023; 157 Piovan (10.1016/j.optlaseng.2024.108131_bib0002) 2023; 60 Donoho (10.1016/j.optlaseng.2024.108131_bib0010) 1995; 41 Redmon (10.1016/j.optlaseng.2024.108131_bib0021) 2016 Chambi (10.1016/j.optlaseng.2024.108131_bib0020) 2016; 46 Luo (10.1016/j.optlaseng.2024.108131_bib0028) 2021 Sang (10.1016/j.optlaseng.2024.108131_bib0008) 2020; 4 Liu (10.1016/j.optlaseng.2024.108131_bib0004) 2023; 168 Jiang (10.1016/j.optlaseng.2024.108131_bib0023) 2023; 13 Robin (10.1016/j.optlaseng.2024.108131_bib0014) 2020; 5 Zang (10.1016/j.optlaseng.2024.108131_bib0005) 2019; 14 Han (10.1016/j.optlaseng.2024.108131_bib0027) 2022; 45 Zou (10.1016/j.optlaseng.2024.108131_bib0012) 2023; 167 Szegedy (10.1016/j.optlaseng.2024.108131_bib0013) 2016 Ren (10.1016/j.optlaseng.2024.108131_bib0015) 2015; 37 Wang (10.1016/j.optlaseng.2024.108131_bib0026) 2022; 70 Wang (10.1016/j.optlaseng.2024.108131_bib0007) 2023; 8 Ren (10.1016/j.optlaseng.2024.108131_bib0003) 2022; 187 Liu (10.1016/j.optlaseng.2024.108131_bib0001) 2023; 48 Casasanta (10.1016/j.optlaseng.2024.108131_bib0016) 2018; 2 Dai (10.1016/j.optlaseng.2024.108131_bib0019) 2022; 14 Wang (10.1016/j.optlaseng.2024.108131_bib0011) 2020; 11567 Zhang (10.1016/j.optlaseng.2024.108131_bib0024) 2019; 11 Xu (10.1016/j.optlaseng.2024.108131_bib0017) 2023; 130 |
| References_xml | – start-page: 770 year: 2016 end-page: 778 ident: bib0025 article-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition publication-title: Deep residual learning for image recognition – volume: 167 year: 2023 ident: bib0012 article-title: Target recognition based on pre-processing in computational ghost imaging with deep learning publication-title: Opt Laser Technol – volume: 48 start-page: 2587 year: 2023 end-page: 2590 ident: bib0001 article-title: Scale-adaptive three-dimensional imaging using Risley-prism-based coherent lidar publication-title: Opt Lett – start-page: 779 year: 2016 end-page: 788 ident: bib0021 article-title: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition publication-title: You only look once: unified, real-time object detection – volume: 60 year: 2023 ident: bib0002 article-title: LiDAR-change-based map** of sediment movement from an extreme rainfall event publication-title: GIsci Remote Sens – volume: 14 start-page: 1 year: 2022 end-page: 11 ident: bib0019 article-title: GCD-YOLOv5: an armored target recognition algorithm in complex environments based on array Lidar publication-title: IEEE Photonics J – volume: 37 start-page: 1 year: 2015 end-page: 4 ident: bib0015 article-title: Anti-interference of dual-wavelength laser fuze[J] publication-title: J Detect Control – volume: 14 start-page: 103 year: 2019 end-page: 111 ident: bib0005 article-title: The impact of adverse weather conditions on autonomous vehicles: how rain, snow, fog, and hail affect the performance of a self-driving car publication-title: IEEE Veh Technol Mag – volume: 46 start-page: 709 year: 2016 end-page: 719 ident: bib0020 article-title: Better bitmap performance with roaring bitmaps publication-title: Softw Pract Exp – start-page: 15283 year: 2021 end-page: 15292 ident: bib0018 article-title: Proceedings of the IEEE/CVF International Conference on Computer Vision publication-title: Fog simulation on real LiDAR point clouds for 3D object detection in adverse weather – volume: 168 year: 2023 ident: bib0004 article-title: Polarised full-waveform warning LIDAR with dust backscattering suppression publication-title: Opt Lasers Eng – volume: 11567 start-page: 811 year: 2020 end-page: 816 ident: bib0011 article-title: Laser detection technology based on wavefront measurement publication-title: Proceedings of the conference on optical sensing and imaging technology – volume: 41 start-page: 613 year: 1995 end-page: 627 ident: bib0010 article-title: De-noising by soft-thresholding publication-title: IEEE Trans Inf Theory – start-page: 2818 year: 2016 end-page: 2826 ident: bib0013 article-title: Rethinking the inception architecture for computer vision publication-title: Proceedings of the IEEE conference on computer vision and pattern recognition – volume: 14 start-page: 4960 year: 2022 ident: bib0006 article-title: A novel lidar signal-denoising algorithm based on sparrow search algorithm for optimal variational modal decomposition publication-title: Remote Sens – volume: 70 start-page: 5217 year: 2022 end-page: 5226 ident: bib0026 article-title: Cone-shaped space target inertia characteristics identification by deep learning with compressed dataset publication-title: IEEE Trans Antennas Propag – volume: 12274 start-page: 23 year: 2022 end-page: 32 ident: bib0009 article-title: An image feature-based approach to improving SPAD flash lidar imaging through fog publication-title: Proceedings of the emerging imaging and sensing technologies for security and defence VII – volume: 11 start-page: 997 year: 2019 ident: bib0024 article-title: A novel noise suppression channel estimation method based on adaptive weighted averaging for OFDM systems publication-title: Symmetry – volume: 187 year: 2022 ident: bib0003 article-title: Adaptive Doppler compensation method for coherent LIDAR based on optical phase-locked loop publication-title: Measurement – volume: 130 start-page: 104613 year: 2023 ident: bib0017 article-title: Full-waveform LiDAR echo decomposition method based on deep learning and sparrow search algorithm publication-title: Infrared Phys Technol – volume: 13 start-page: 3772 year: 2023 ident: bib0023 article-title: Adaptive suppression method of lidar background noise based on threshold detection publication-title: Appl Sci – start-page: 638 year: 2021 end-page: 645 ident: bib0028 article-title: A single-photon lidar ranging accuracy evaluation model publication-title: Proceedings of the Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 11763 – volume: 5 start-page: 2514 year: 2020 end-page: 2521 ident: bib0014 article-title: CNN-based Lidar point cloud de-noising in adverse weather publication-title: IEEE Robot Autom Lett – volume: 2 start-page: 8 year: 2018 ident: bib0016 article-title: Towards a generalized Beer-Lambert law publication-title: Fractal Fract – volume: 8 start-page: 6675 year: 2023 end-page: 6682 ident: bib0007 article-title: SW-LIO: a sliding window based tightly coupled LiDAR-inertial odometry publication-title: IEEE Robot Autom Lett – volume: 157 start-page: 108749 year: 2023 ident: bib0022 article-title: Single-photon Lidar for canopy detection with a multi-channel Si SPAD at 1064 nm publication-title: Opt Laser Technol – volume: 45 start-page: 87 year: 2022 end-page: 110 ident: bib0027 article-title: A survey on vision transformer publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 4 start-page: 1 year: 2020 end-page: 4 ident: bib0008 article-title: Mitigating effects of uniform fog on SPAD lidars publication-title: IEEE Sens Lett – start-page: 779 year: 2016 ident: 10.1016/j.optlaseng.2024.108131_bib0021 article-title: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition publication-title: You only look once: unified, real-time object detection – volume: 14 start-page: 4960 issue: 19 year: 2022 ident: 10.1016/j.optlaseng.2024.108131_bib0006 article-title: A novel lidar signal-denoising algorithm based on sparrow search algorithm for optimal variational modal decomposition publication-title: Remote Sens doi: 10.3390/rs14194960 – volume: 2 start-page: 8 issue: 1 year: 2018 ident: 10.1016/j.optlaseng.2024.108131_bib0016 article-title: Towards a generalized Beer-Lambert law publication-title: Fractal Fract doi: 10.3390/fractalfract2010008 – volume: 130 start-page: 104613 year: 2023 ident: 10.1016/j.optlaseng.2024.108131_bib0017 article-title: Full-waveform LiDAR echo decomposition method based on deep learning and sparrow search algorithm publication-title: Infrared Phys Technol doi: 10.1016/j.infrared.2023.104613 – start-page: 15283 year: 2021 ident: 10.1016/j.optlaseng.2024.108131_bib0018 article-title: Proceedings of the IEEE/CVF International Conference on Computer Vision – volume: 60 issue: 1 year: 2023 ident: 10.1016/j.optlaseng.2024.108131_bib0002 article-title: LiDAR-change-based map** of sediment movement from an extreme rainfall event publication-title: GIsci Remote Sens doi: 10.1080/15481603.2023.2227394 – volume: 48 start-page: 2587 issue: 10 year: 2023 ident: 10.1016/j.optlaseng.2024.108131_bib0001 article-title: Scale-adaptive three-dimensional imaging using Risley-prism-based coherent lidar publication-title: Opt Lett doi: 10.1364/OL.487477 – volume: 14 start-page: 1 issue: 4 year: 2022 ident: 10.1016/j.optlaseng.2024.108131_bib0019 article-title: GCD-YOLOv5: an armored target recognition algorithm in complex environments based on array Lidar publication-title: IEEE Photonics J doi: 10.1109/JPHOT.2022.3185304 – volume: 187 year: 2022 ident: 10.1016/j.optlaseng.2024.108131_bib0003 article-title: Adaptive Doppler compensation method for coherent LIDAR based on optical phase-locked loop publication-title: Measurement doi: 10.1016/j.measurement.2021.110313 – volume: 12274 start-page: 23 year: 2022 ident: 10.1016/j.optlaseng.2024.108131_bib0009 article-title: An image feature-based approach to improving SPAD flash lidar imaging through fog – volume: 167 year: 2023 ident: 10.1016/j.optlaseng.2024.108131_bib0012 article-title: Target recognition based on pre-processing in computational ghost imaging with deep learning publication-title: Opt Laser Technol doi: 10.1016/j.optlastec.2023.109807 – volume: 70 start-page: 5217 issue: 7 year: 2022 ident: 10.1016/j.optlaseng.2024.108131_bib0026 article-title: Cone-shaped space target inertia characteristics identification by deep learning with compressed dataset publication-title: IEEE Trans Antennas Propag doi: 10.1109/TAP.2022.3172759 – volume: 45 start-page: 87 issue: 1 year: 2022 ident: 10.1016/j.optlaseng.2024.108131_bib0027 article-title: A survey on vision transformer publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2022.3152247 – volume: 157 start-page: 108749 year: 2023 ident: 10.1016/j.optlaseng.2024.108131_bib0022 article-title: Single-photon Lidar for canopy detection with a multi-channel Si SPAD at 1064 nm publication-title: Opt Laser Technol doi: 10.1016/j.optlastec.2022.108749 – volume: 14 start-page: 103 issue: 2 year: 2019 ident: 10.1016/j.optlaseng.2024.108131_bib0005 article-title: The impact of adverse weather conditions on autonomous vehicles: how rain, snow, fog, and hail affect the performance of a self-driving car publication-title: IEEE Veh Technol Mag doi: 10.1109/MVT.2019.2892497 – volume: 5 start-page: 2514 issue: 2 year: 2020 ident: 10.1016/j.optlaseng.2024.108131_bib0014 article-title: CNN-based Lidar point cloud de-noising in adverse weather publication-title: IEEE Robot Autom Lett doi: 10.1109/LRA.2020.2972865 – volume: 41 start-page: 613 issue: 3 year: 1995 ident: 10.1016/j.optlaseng.2024.108131_bib0010 article-title: De-noising by soft-thresholding publication-title: IEEE Trans Inf Theory doi: 10.1109/18.382009 – volume: 13 start-page: 3772 issue: 6 year: 2023 ident: 10.1016/j.optlaseng.2024.108131_bib0023 article-title: Adaptive suppression method of lidar background noise based on threshold detection publication-title: Appl Sci doi: 10.3390/app13063772 – start-page: 2818 year: 2016 ident: 10.1016/j.optlaseng.2024.108131_bib0013 article-title: Rethinking the inception architecture for computer vision – volume: 11567 start-page: 811 year: 2020 ident: 10.1016/j.optlaseng.2024.108131_bib0011 article-title: Laser detection technology based on wavefront measurement – volume: 168 year: 2023 ident: 10.1016/j.optlaseng.2024.108131_bib0004 article-title: Polarised full-waveform warning LIDAR with dust backscattering suppression publication-title: Opt Lasers Eng doi: 10.1016/j.optlaseng.2023.107658 – start-page: 638 year: 2021 ident: 10.1016/j.optlaseng.2024.108131_bib0028 article-title: A single-photon lidar ranging accuracy evaluation model – volume: 46 start-page: 709 issue: 5 year: 2016 ident: 10.1016/j.optlaseng.2024.108131_bib0020 article-title: Better bitmap performance with roaring bitmaps publication-title: Softw Pract Exp doi: 10.1002/spe.2325 – volume: 4 start-page: 1 issue: 9 year: 2020 ident: 10.1016/j.optlaseng.2024.108131_bib0008 article-title: Mitigating effects of uniform fog on SPAD lidars publication-title: IEEE Sens Lett doi: 10.1109/LSENS.2020.3018708 – volume: 11 start-page: 997 issue: 8 year: 2019 ident: 10.1016/j.optlaseng.2024.108131_bib0024 article-title: A novel noise suppression channel estimation method based on adaptive weighted averaging for OFDM systems publication-title: Symmetry doi: 10.3390/sym11080997 – start-page: 770 year: 2016 ident: 10.1016/j.optlaseng.2024.108131_bib0025 article-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 8 start-page: 6675 issue: 10 year: 2023 ident: 10.1016/j.optlaseng.2024.108131_bib0007 article-title: SW-LIO: a sliding window based tightly coupled LiDAR-inertial odometry publication-title: IEEE Robot Autom Lett doi: 10.1109/LRA.2023.3311371 – volume: 37 start-page: 1 issue: 1 year: 2015 ident: 10.1016/j.optlaseng.2024.108131_bib0015 article-title: Anti-interference of dual-wavelength laser fuze[J] publication-title: J Detect Control |
| SSID | ssj0016411 |
| Score | 2.4395785 |
| Snippet | •YMPL can accurately detect targets in fog and expand the detection range.•New data preprocessing for creating bitmaps from multiple pulses.•The data... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 108131 |
| SubjectTerms | Foggy detection Lidar YOLO target recognition algorithm |
| Title | The YOLO-based Multi-Pulse Lidar (YMPL) for target detection in hazy weather |
| URI | https://dx.doi.org/10.1016/j.optlaseng.2024.108131 |
| Volume | 177 |
| WOSCitedRecordID | wos001199823400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: ScienceDirect Freedom Collection - Elsevier customDbUrl: eissn: 1873-0302 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016411 issn: 0143-8166 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3fb9MwELagAwkeEAwQg4H8wAOoctUkTmLzNk0bP1TWPgwpfYoS2xmbUFqtCZT99TvHjpPCpLIHXqLopHOa3Nfz5_P5DqG3io0jXYaO5IUUhLJCEJYXjLAsLiKfU-UJ0TSbiE9OWJLwmd2KWTXtBOKyZOs1X_5XU4MMjK2Pzt7C3G5QEMA9GB2uYHa4_rPh59PJlOgJSg6bE7ZkVsMUCAtwmel-Gmz-dTbR8YAmx7DJBR9KVSnRZj5-z65-D38Zdtinr9Olq-oMrFuf_dUVR7qShs7F1816f9FJPtrU3-N6DYB04rkNVyd1K0mMat0PR_i0S5tyEcqA6M3IDRdrW7UYJ-kBDTGu_y__bUIJF6PFstKvUZ6N9DNGncZmxew_ZjKXX9imrl2kbqBUD5Sage6iHT8OORugnYPPR8kXt-0UUc80sLTvsJEQeONvupnO9CjK6WP0yK4t8IHBxBN0R5W76GGv4uQuut9k_IrVUzQBnOAOJ7iHE9zgBL_TKHmPASPYYAQ7jODzEmuMYIuRZ-jb8dHp4SdiW2sQEXhhRXKWjYWUNPRzP1RAOhUPCi4LrmhAuZRx7EmfM1_BglVv9FIvllRJHohYqkCEwXM0KBeleoEwpZFkmndKxWjuZ1lEx5IJFcH308ek91DUfqFU2Lrzuv3Jj3SLlfbQ2CkuTemV7SofWhOklkEaZpgCwLYpv7z9816hB91_YB8NqstavUb3xM_qfHX5xqLrGl6DksY |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=The+YOLO-based+Multi-Pulse+Lidar+%28YMPL%29+for+target+detection+in+hazy+weather&rft.jtitle=Optics+and+lasers+in+engineering&rft.au=Wu%2C+Long&rft.au=Gong%2C+Fuxiang&rft.au=Yang%2C+Xu&rft.au=Xu%2C+Lu&rft.date=2024-06-01&rft.issn=0143-8166&rft.volume=177&rft.spage=108131&rft_id=info:doi/10.1016%2Fj.optlaseng.2024.108131&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_optlaseng_2024_108131 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0143-8166&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0143-8166&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0143-8166&client=summon |