[논문]Fast, Accurate Vehicle Detection and Distance Estimation

Ma, QuanMeng; Jiang, Guang; Lai, DianZhi; cui, Hua; Song, Huansheng

doi:10.3837/tiis.2020.02.008

Fast, Accurate Vehicle Detection and Distance Estimation 원문보기

KSII Transactions on internet and information systems : TIIS, v.14 no.2, 2020년, pp.610 - 630

Ma, QuanMeng (School of Telecommunication Engineering, Xidian University) , Jiang, Guang (School of Telecommunication Engineering, Xidian University) , Lai, DianZhi (School of Telecommunication Engineering, Xidian University) , cui, Hua (School of information engineering, Chang'an University) , Song, Huansheng (School of information engineering, Chang'an University)

Abstract ▼ AI-Helper

A large number of people suffered from traffic accidents each year, so people pay more attention to traffic safety. However, the traditional methods use laser sensors to calculate the vehicle distance at a very high cost. In this paper, we propose a method based on deep learning to calculate the vehicle distance with a monocular camera. Our method is inexpensive and quite convenient to deploy on the mobile platforms. This paper makes two contributions. First, based on Light-Head RCNN, we propose a new vehicle detection framework called Light-Car Detection which can be used on the mobile platforms. Second, the planar homography of projective geometry is used to calculate the distance between the camera and the vehicles ahead. The results show that our detection system achieves 13FPS detection speed and 60.0% mAP on the Adreno 530 GPU of Samsung Galaxy S7, while only requires 7.1MB of storage space. Compared with the methods existed, the proposed method achieves a better performance.

주제어

표/그림 (22)

그림 Fig. 1. Some results predicted by our method, TD represents the true distance, and PD is the estimated distance.
그림 Fig. 2. The implementation steps of the proposed framework.
표 Table 1. The network structure of MobileNetV1.
표 Table 2. Performance comparison of Light-Head R-CNN and SSD on NEXAR2 validation set.
그림 Fig. 3. Overview of three different structures.
표 Table 3. Comparison of the parameters and the computational costs of three different structures with w be 8 and h be 30 on NEXAR2 validation set.
표 Table 4. Performance comparison of large separable convolution and bottleneck on NEXAR2 validation set.
표 Table 5. The modified RPN performance has improved. The performance is evaluated on NEXAR2 validation set.
표 Table 6. Performance comparison of Light-Head R-CNN and Light-Car Detection on NEXAR2 validation set.
그림 Fig. 4. Sorting filters by ratios for each layer of Light-Car Detection on nexar2.
표 Table 7. The number of parameters, accuracy and running time after pruning filters. The performance is evaluated on NEXAR2 validation set.
그림 Fig. 5. The calculation method of homography transformation between the flat ground plane and the image, p is the center point of the lower edge of bounding box, so the distance is the y-coordinate of P.
그림 Fig. 6. Some examples of Light-Car Detection on the KITTI dataset.
그림 Fig. 7. The 3D car is surrounded by a 3D blue box, 4 tail points on the rear of the 3D box are projected to the image to create ground truth box.
그림 Fig. 8(a). Scale and ratio Error with respect to the overlap threshold¹
그림 Fig. 8(b). Center Position Error with respect to the overlap threshold¹
표 Table 8. Inference time on Samsung Galaxy S7.
표 Table 9. Mean Absolute Error (in meters) on KITTI 3D object detection dataset.
그림 Fig. 9. The difference between the obtained distance and the true value at different car distances.
그림 Fig. 10. Some examples of our method on smart phone.
표 Table 10. The value of Mean Absolute Error (in meters) in different distance.
표 Table 11. Comparison between our method and the method using monocular depth estimation on the KITTI dataset. The best results are bolded.

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