HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation

Daichao Zhao; Qiupu Chen; Feng He; Xin Ning; Qiankun Li

cs.CVMar 10, 2026

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation

Authors: Daichao Zhao, Qiupu Chen, Feng He, Xin Ning, Qiankun Li

Organizations: Shanghai Jiao Tong University · School of Artificial Intelligence, Henan University · University of Science and Technology of China · AnnLab, Institute of Semiconductors, Chinese Academy of Sciences ·

Abstract

Lane detection is a crucial task in autonomous driving, as it helps ensure the safe operation of vehicles. However, existing datasets such as CULane and TuSimple contain relatively limited data under extreme weather conditions, including rain, snow, and fog. As a result, detection models trained on these datasets often become unreliable in such environments, which may lead to serious safety-critical failures on the road. To address this issue, we propose HG-Lane, a High-fidelity Generation framework for Lane Scenes under adverse weather and lighting conditions without requiring re-annotation. Based on this framework, we further construct a benchmark that includes adverse weather and lighting scenarios, containing 30,000 images. Experimental results demonstrate that our method consistently and significantly improves the performance of existing lane detection networks. For example, using the state-of-the-art CLRNet, the overall mF1 score on our benchmark increases by 20.87 percent. The F1@50 score for the overall, normal, snow, rain, fog, night, and dusk categories increases by 19.75 percent, 8.63 percent, 38.8 percent, 14.96 percent, 26.84 percent, 21.5 percent, and 12.04 percent, respectively. The code and dataset are available at: https://github.com/zdc233/HG-Lane.

Figures & tables

Figure 2: Overview of the proposed HG-Lane. The input image is first processed into a fused control map combining color-based masks, Canny edges and lane annotations. In Stage-I, a Canny-ControlNet enforces lane geometry during reverse diffusion in latent space. In Stage-II, an InstructPix2Pix-ControlNet optionally refines appearance for “night” and “dusk”. Finally, the latent is decoded back to pixel space. All components are pretrained; no fine-tuning is required.

Method	Normal	Snow	Rain	Fog	Night	Dusk	F1@50	F1@75	mF1
w/o aug (4k)	83.01	46.08	70.34	58.54	70.09	79.95	68.74	46.39	42.16
w/o aug (4k*6)	87.28	51.79	71.48	58.31	62.67	75.62	68.89	50.87	45.42
w/ aug (4k*6)	91.64	84.88	85.30	85.38	91.59	91.99	88.49	72.79	63.03

Table 1: Performance on CLRNet (ResNet-18). For Normal, Snow, Rain, Fog, Night, and Dusk, the metric used is F1@50.

#	Mask	Canny	iP2P	F1@50	F1@75	mF1
1	✓	✓before	✓after	88.49	72.79	63.03
2	✓	✓after	✓before	62.56	41.01	39.44
3	✓		✓	59.75	40.33	39.33
4	✓	✓		64.01	43.78	40.12
5		✓before	✓after	86.81	70.12	61.35

Table 2: Ablation study. The symbol ✓indicates whether it is used, while ”before” and ”after” denote the sequence of usage.

Figure 3: Results of some baselines. The green lines in the figure represent the predicted values, while blue lines represent ground truth.

Method	Normal	Snow	Rain	Fog	Night	Dusk	F1@50	F1@75	mF1
Classical / Light Backbones
SCNN (ResNet-34) [ 23 ]	75.91	68.96	67.56	62.54	70.51	70.46	72.97	31.34	20.08
SAD (ENet) [ 15 ]	76.75	64.71	68.85	61.73	73.39	76.68	70.02	30.91	19.36
LSTR (ResNet-34) [ 19 ]	84.06	74.67	76.61	74.40	83.71	84.38	65.07	59.75	16.48
UFLD / RESA Family
UFLD (ResNet-18) [ 28 ]	69.59	59.29	60.77	56.52	59.69	63.97	61.65	17.56	13.42

Table 3: Performance comparison on CULane benchmarks. For Normal, Snow, Rain, Fog, Night, and Dusk, the metric is F1@50 to provide a clearer comparison across different environmental conditions. Best results are in bold , second best are underlined .

Figure 4: Ablation Study. Experiment #1, #2, #3, and #4 demonstrate the generation results of using Canny or InstructPix2Pix individually, as well as the effects of their combined order.

Method	F1@50 $\uparrow$	FID $\downarrow$	CLIP-T score $\uparrow$	Human score $\uparrow$
Stable Diffusion	69.02	236.7	0.20	2.35 $\pm$ 0.75
PITI	58.50	390.5	0.16	1.1 $\pm$ 0.31
CycleGAN	81.49	99.8	0.22	3.75 $\pm$ 0.55
Flux.1	70.04	120.3	0.21	2.5 $\pm$ 0.61
HG-Lane(Ours)	88.49	80.1	0.23	4.15 $\pm$ 0.59

Table 4: Generative Quality. The symbol

\uparrow

indicates that the higher the value of item, the better. The symbol

\downarrow

is opposite.

Figure 5: Quality Analysis of Generation. Comparison of images generated by different frameworks. The figure illustrates the generation results of different frameworks across five categories (dusk, fog, rain, night, and snow).

Train on	Snow	Rain	Fog	Night	Dusk	F1@50	F1@75	mF1
CULane	35.41	74.78	50.00	22.47	20.77	53.03	21.62	25.65
HG-Lane(Ours)	55.65	77.58	65.57	60.02	69.31	68.54	27.48	32.75

Table 5: Performance on CLRNet (ResNet-18) in Real world. For Normal, Snow, Rain, Fog, Night, and Dusk, the metric used is F1@50.

Category	Method	F1@50	Time(ms/img)
DeRain	EfficientDeRain	74.82	4.8
	NeRD-Rain	74.86	9.2
	HG-Lane(Ours)	77.58	0
DeSnow	DeSnowNet	35.21	10.2
	HDCW-Net	37.21	6.3
	HG-Lane(Ours)	55.65	0

Table 6: Performance on CLRNet (ResNet-18).

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 6: Comparison with Real-World Samples.

Figure 7: Comparison with Suppression Module.

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May 12, 2026J/K move · Enter open · S save

XWOD: A Real-World Benchmark for Object Detection under Extreme Weather Conditions

Chih-Hsin Chen, Yu-Tung Liu, Amar Fadillah, Kuan-Ting Lai+1

Department of Electronic Engineering, National Taipei University of Technology · Adobe Inc.

Autonomous Driving Datasets Autonomous Driving Benchmarks Autonomous Driving Perception Object Detection

Open paper AskPDF arXiv

Autonomous driving and intelligent transportation systems remain vulnerable under extreme weather. The U.S. Federal Highway Administration reports that roughly 745,000 crashes and 3,800 fatalities per year are weather-related, and recent regulatory investigations have examined failures of Level-2/3 driving systems under reduced-visibility conditions. However, datasets commonly used to evaluate weather robustness remain limited in scale, diversity, and realism. In this paper, we introduce XWOD (Extreme Weather Object Detection), a large-scale real-world traffic-object detection benchmark containing 10,010 images and 42,924 bounding boxes across seven extreme weather conditions: rain, snow, fog, haze/sand/dust, flooding, tornado, and wildfire. The dataset covers six traffic-object categories, including car, person, truck, motorcycle, bicycle, and bus. XWOD extends the weather taxonomy from one to seven conditions, and is the first to cover the emerging class of climate-amplified hazards, such as flooding, tornado, and wildfire. To evaluate the quality of our data, we train standard YOLO-family detectors on XWOD and test them zero-shot on external weather benchmarks, achieving mAP

_{50}

scores of 63.00% on RTTS, 59.94% on DAWN, and 61.12% on WEDGE, compared with the corresponding published YOLO-based baselines of 40.37%, 32.75%, and 45.41%, respectively, representing relative improvements of 56%, 83%, and 35%. These cross-dataset results show that XWOD provides a strong source domain for learning weather-robust traffic perception. We release the dataset, splits, baseline weights, and reproducible evaluation code under a research-use license.

Abstract

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