Recognising daytime and nighttime driving images using Bayes classifier

Hsieh, He-Yen; Chen, N.

doi:10.1049/iet-its.2010.0153

Cited by 6 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method first judges whether the image is positively guiding the completion of the sparse depth map based on whether the acquired image is day or night. Here, there are many methods of day-night image classification, such as Bayesian classifier [32], SVM classifier, and CNN. When it is daytime, image-guided depth completion is used, and at night, only LiDAR data is used for completion.…”

Section: Figure 2 the Coordinate Conversion Between The Image And Thmentioning

confidence: 99%

“…Compared with MS-CNN, SubCNN, 3DOP, and Mono3D methods with high AP, our method is 7×, 35×, 53×, and 73× faster, respectively. [7] 90.09% 88.34% 78.39% 77.42% 3s GPU@2.5Ghz MS-CNN [5] 90.03% 89.02% 76.11% 73.57% 0.4s GPU@2.5Ghz Yolov2 [32] 74.35% 62.65% 53.23% 56.45% 0.03s GPU@3.5Ghz Yolov3 [24] 82.17% 77.53% 68.47% 65.47% 0.04s GPU@2.5Ghz R-SSD [41] 88 For nighttime detection, the method in this paper still has excellent performance, ranking third in detection precision and third in detection speed among all the compared methods.…”

Section: Color Non-guided Depth Completion Fusion Ap 6547%mentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Vehicle Detection Framework Based on the Fusion of LiDAR and Camera

et al. 2020

View full text Add to dashboard Cite

Vehicle detection is essential for driverless systems. However, the current single sensor detection mode is no longer sufficient in complex and changing traffic environments. Therefore, this paper combines camera and light detection and ranging (LiDAR) to build a vehicle-detection framework that has the characteristics of multi adaptability, high real-time capacity, and robustness. First, a multi-adaptive high-precision depth-completion method was proposed to convert the 2D LiDAR sparse depth map into a dense depth map, so that the two sensors are aligned with each other at the data level. Then, the You Only Look Once Version 3 (YOLOv3) real-time object detection model was used to detect the color image and the dense depth map. Finally, a decision-level fusion method based on bounding box fusion and improved Dempster–Shafer (D–S) evidence theory was proposed to merge the two results of the previous step and obtain the final vehicle position and distance information, which not only improves the detection accuracy but also improves the robustness of the whole framework. We evaluated our method using the KITTI dataset and the Waymo Open Dataset, and the results show the effectiveness of the proposed depth completion method and multi-sensor fusion strategy.

show abstract

Section: Figure 2 the Coordinate Conversion Between The Image And Thmentioning

confidence: 99%