不确定性建模在2D和3D目标检测中的应用

doi:10.12305/j.issn.1001-506X.2023.08.10

Abstract

Abstract:

Object detection algorithm plays an indispensable role in the field of autopilot. Its detection accuracy and speed can often be used as the standard to judge the quality of an auto drive system. How to improve the accuracy and speed of object detection has become the main research direction of the current object detection algorithm. Therefore, an improved object detection algorithm based on uncertainty modeling is proposed. Based on the original two-dimensional (2D) single shot multibox detector (SSD) object detection algorithm, the original detection results are finely tuned by Gaussian modeling the boundary box of the object and introducing a new position loss function. At the same time, based on the original three-dimensional (3D) single-stage monocular (SMOKE) object detection algorithm, the uncertainty of depth and heading angle is introduced to adjust the Gaussian radius of the target in the heatmap, and the Mahalanobis distance is used to replace the original L1 distance to improve the recognition rate of distant targets and oblique targets. Comparative experiments proves that the detection accuracy of the improved 2D and 3D detection algorithms is improved by nearly 5% and 2% respectively compared with the original 2D and 3D algorithms on the KITTI autonomous driving dataset.

Key words: deep learning, object detection, autonomous driving, uncertainty modeling

CLC Number:

TP391.41

Meng WANG, Bing ZHU. Application of uncertainty modeling in 2D and 3D object detection[J]. Systems Engineering and Electronics, 2023, 45(8): 2370-2376.

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模型	汽车-AP/%	行人-AP/%	自行车-AP/%	货车-AP/%	mAP/%	FPS
SSD	85.7	41.6	51.4	86.8	66.4	125.7
SSD+不确定性	88.9	46.8	59.9	89.7	71.4	103.8

模型	车	行人	自行车
SMOKE	10.27	2.35	3.48
SMOKE+不确定性	12.08	1.96	3.56

模型	简单-AP%	中等-AP%	困难-AP%	FPS
SMOKE	13.29	9.55	7.26	32.30
SMOKE+不确定性	14.52	10.57	8.76	30.50
文献[20]	10.00	7.70	5.30	55.20
文献[21]	10.50	5.70	5.40	-

模型	简单-AP%	中等-AP%	困难-AP%	FPS
SMOKE	19.65	14.77	14.32	32.30
SMOKE+不确定性	21.28	15.16	14.45	30.50
文献[20]	10.00	7.70	5.30	55.20
文献[21]	22.00	13.60	11.60	-

模型	简单-AP%	中等-AP%	困难-AP%	FPS
SMOKE	87.25	82.61	65.45	32.30
SMOKE+不确定性	86.79	83.27	68.72	30.50

Application of uncertainty modeling in 2D and 3D object detection

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