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...

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Vydané v:Optics and lasers in engineering Ročník 177; s. 108131
Hlavní autori: Wu, Long, Gong, Fuxiang, Yang, Xu, Xu, Lu, Chen, Shuyu, Zhang, Yong, Zhang, Jianlong, Yang, Chenghua, Zhang, Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.06.2024
Predmet:
Foggy detection
Lidar
YOLO target recognition algorithm
Lidar
Foggy detection
YOLO target recognition algorithm
ISSN:0143-8166, 1873-0302
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Popis
Shrnutí:•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.
ISSN:0143-8166
1873-0302
DOI:10.1016/j.optlaseng.2024.108131