基于双目视觉的道路水深在线检测算法

doi:10.13229/j.cnki.jdxbgxb.20230325

Abstract

Abstract:

Wading driving poses risks to the life and property safety of drivers and passengers. To improve the safety of wading driving， a technical solution combining binocular vision and laser projection was adopted. Based on the imaging law of underwater virtual images， a binocular vision algorithm for calculating the depth of water ahead was proposed， which only requires data collected by the vehicle's sensors. To verify the effectiveness of the algorithm， experimental verification is conducted. The results showed that under ideal conditions， the relative error of the algorithm's calculation results was within 3%. In practical scenarios， the methods of averaging and filtering can reduce the impact of water surface fluctuations， and the maximum relative error introduced by turbid water is about 7%. This proves that the algorithm proposed has good accuracy and robustness and can provide drivers with water depth information ahead to assist drivers in judging road conditions.

Key words: vehicle engineering, water depth detection, binocular vision, driving aids, coordinate transformation, laser image spot

CLC Number:

U471.15

Jun-nian WANG,Yu-jing CAO,Zhi-ren LUO,Kai-xuan LI,Wen-bo ZHAO,Ying-yi MENG. Online detection algorithm of road water depth based on binocular vision[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(1): 175-184.

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参数	实验序号
参数	1	2	3	4	5	6	7	8	9	10	平均值
z_P /mm	500	500	550	550	600	600	650	650	700	700
B/mm	100	120	100	120	100	80	100	90	100	110
n_x_l/像素	203	331	155	273	146	38	149	100	131	176
n_x_r/像素	-445	-445	-434	-434	-392	-392	-349	-349	-332	-332
n_f /像素	3 240	3 233	3 240	3 240	3 228	3 225	3 237	3 243	3 241	3 233	3 236

参数	实验序号
参数	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
b_O /mm	223	223	223	191	191	190	156	156	161	119	119	121	92	92	95
α/弧度	0.6	0.6	0.6	0.6	0.6	0.66	0.66	0.66	0.48	0.48	0.48	0.42	0.42	0.42	0.3
B/mm	100	140	120	100	90	100	100	80	100	100	150	100	100	150	100
n_x_l/像素	578	966	772	494	407	454	477	301	432	445	910	446	540	1020	579
n_y_l/像素	-1 009	-1 004	-1 007	-490	-490	-281	-169	-171	-845	-694	-674	-921	-783	-753	-1 278
n_x_r/像素	-386	-386	-386	-362	-362	-401	-397	-397	-466	-475	-475	-486	-404	-404	-400
n_y_r/像素	-1 006	-1 006	-1 006	-482	-482	-280	-162	-162	-849	-697	-697	-926	-786	-786	-1 272
H/mm	82	82	82	115	115	115	150	150	150	192	192	192	222	222	222
h₁/mm	80	80	80	115	116	112	146	147	147	189	189	189	219	217	222
误差/mm	-2	-2	-2	0	1	-3	-4	-3	-3	-3	-3	-3	-3	-5	0
相对误差/%	-2.4	-2.4	-2.4	0.0	0.9	-2.6	-2.7	-2.0	-2.0	-1.6	-1.6	-1.6	-1.4	-2.3	0.0
h₂/mm	53	52	52	70	70	68	86	87	89	110	108	111	124	119	127
误差/mm	-29	-30	-30	-45	-45	-47	-64	-63	-61	-82	-84	-81	-98	-103	-95
相对误差/%	-35	-37	-37	-39	-39	-41	-43	-42	-41	-43	-44	-42	-44	-46	-43

参数	实验序号
参数	1	2	3	4	5	6	7	8	9	10	后9组平均值
n_x_l/像素	269	271	274	265	264	260	271	272	268	263
n_y_l/像素	-956	-953	-957	-961	-957	-960	-957	-956	-955	-966
n_x_r/像素	-627	-633	-629	-626	-629	-629	-623	-625	-634	-626
n_y_r/像素	-941	-951	-939	-944	-942	-948	-961	-945	-957	-939
h₀/mm	136	136	136	136	136	136	136	136	136	136
h/mm	136	131	133	139	137	139	135	135	132	141	135.78
误差/mm	0	-5	-3	3	1	3	-1	-1	-4	5	-0.22
相对误差/%	0.0	-3.7	-2.2	2.2	0.7	2.2	-0.7	-0.7	-2.9	3.7	-0.2

参数	实验序号
参数	1	2	3	4	5	6	7	8
浊度/NTU	0.7	5.9	6.8	9.8	15.0	16.8	17.7	19.3
n_x_l/像素	253	253	253	254	253	252	253	253
n_y_l/像素	-10	-6	-8	4	6	9	7	11
n_x_r/像素	-29	-29	-30	-29	-29	-31	-31	-30
n_y_r/像素	-8	-3	-6	6	8	11	10	14
h₀/mm	108	108	108	108	108	108	108	108
h/mm	108	107	107	103	104	102	101	101
误差/mm	0	-1	-1	-5	-4	-6	-7	-7
相对误差/%	0.0	-0.9	-0.9	-4.6	-3.7	-5.6	-6.5	-6.5

Online detection algorithm of road water depth based on binocular vision

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