Research on the Expanded Night Road Condition Dataset Based on the Improved CycleGAN

Lei Cao; Li Zhao

doi:10.2478/ijanmc-2024-0016

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Research on the Expanded Night Road Condition Dataset Based on the Improved CycleGAN

International Journal of Advanced Network, Monitoring and Controls

Volume 9 (2024): Issue 2 (June 2024)

By: Lei Cao and Li Zhao

Open Access

|Jul 2024

Figures & Tables

Diagram of the structure of the discriminating network

TABLE I.

Table of individual loss values

epoch	D_A	G_A	cycle_A	idt_A	D_B	G_B	cycle_B	idt_B
epoch:1	0.31	0.34	2.83	1.31	0.32	0.45	2.94	1.43
epoch:1	0.37	0.41	2.46	0.61	0.28	0.36	1.23	1.09
epoch:1	0.28	0.40	1.93	1.46	0.31	0.23	3.20	0.80
epoch:1	0.20	0.42	2.24	1.30	0.23	0.29	3.00	1.01
epoch:2	0.24	0.17	2.16	1.10	0.23	0.27	2.55	0.99
epoch:2	0.22	0.52	2.89	1.37	0.27	0.32	3.06	1.16
epoch:2	0.29	0.20	2.85	1.21	0.32	0.18	2.72	1.40
epoch:2	0.22	0.41	1.85	1.18	0.42	0.61	2.30	0.96
epoch:3	0.18	0.57	1.50	0.96	0.23	0.20	2.21	0.76
epoch:3	0.19	0.88	2.03	1.05	0.15	0.42	2.22	0.85
epoch:3	0.24	0.52	2.14	0.50	0.25	0.37	1.22	1.02
epoch:3	0.43	0.24	1.66	0.90	0.25	0.49	1.76	0.82
epoch:4	0.33	0.23	2.37	0.99	0.49	0.62	2.53	1.06
epoch:4	0.23	0.45	2.50	0.79	0.30	0.66	1.61	1.16
epoch:4	0.08	0.60	1.69	1.33	0.28	0.76	3.02	0.80
epoch:4	0.24	0.25	3.64	1.09	0.42	0.66	3.10	1.58
epoch:5	0.36	0.72	1.34	0.72	0.43	0.87	1.66	0.68
epoch:5	0.10	0.35	3.81	0.73	0.34	0.15	1.82	1.75
epoch:5	0.11	0.35	4.24	0.88	0.30	0.91	1.78	2.15
epoch:5	0.14	0.60	1.26	1.10	0.20	0.24	2.22	0.63
epoch:6	0.16	0.28	1.60	0.93	0.12	0.34	1.79	0.71
epoch:6	0.06	0.28	1.27	0.80	0.25	0.40	1.59	0.61
epoch:6	0.14	0.53	2.40	0.91	0.20	0.40	1.86	1.34
epoch:6	0.16	0.28	2.51	0.90	0.32	0.34	1.93	1.23
epoch:7	0.07	0.22	3.46	0.93	0.34	0.24	1.93	1.30
epoch:7	0.14	0.11	1.99	0.91	0.31	0.25	1.98	0.94
epoch:7	0.41	0.18	1.10	1.10	0.23	0.57	2.01	0.58
epoch:7	0.16	0.18	1.84	0.80	0.16	0.32	1.93	0.83
epoch:8	0.23	0.47	2.64	0.72	0.21	0.23	1.48	1.20
epoch:8	0.25	0.56	1.50	0.65	0.28	0.32	1.18	0.73
epoch:8	0.24	0.46	1.55	0.83	0.18	0.24	1.61	0.70
epoch:8	0.20	0.51	1.44	1.05	0.13	0.34	2.76	0.70
epoch:9	0.20	0.40	1.81	0.83	0.10	0.48	1.63	0.84
epoch:9	0.05	0.59	1.14	0.84	0.20	0.49	1.77	0.42
epoch:9	0.20	0.29	1.70	0.64	0.14	0.05	1.25	0.86
epoch:9	0.43	0.15	1.42	0.86	0.10	0.73	2.14	0.61
epoch:10	0.23	0.73	1.43	0.75	0.12	0.11	2.20	0.74
epoch:10	0.20	0.50	1.65	0.69	0.16	0.57	1.71	0.74
epoch:10	0.34	0.41	2.45	0.73	0.10	0.51	1.26	1.17
epoch:10	0.09	0.43	1.43	1.04	0.16	0.32	1.93	0.71

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DOI: https://doi.org/10.2478/ijanmc-2024-0016 | Journal eISSN: 2470-8038

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Language: English

Page range: 59 - 66

Published on: Jul 21, 2024

Published by: Xi’an Technological University

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Generative,

Adversarial Networks,

Style Migration,

Deep Learning

Related subjects:

Computer sciences,

Computer sciences, other

© 2024 Lei Cao, Li Zhao, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 9 (2024): Issue 2 (June 2024)

Research on the Expanded Night Road Condition Dataset Based on the Improved CycleGAN

Figures & Tables

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

TABLE I.

Figure 8.

Figure 9.

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