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Vehicle Target Detection in Rainy and Foggy Scenes Based on Generative Adversarial Networks and Dynamic Fuzzy Compensation Techniques

Tao Dong

Tao Dong

School of Information Engineering, Liaodong UniversityDandong, China
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Dong, Tao
, 
Xiaojian Liu

Xiaojian Liu

School of Information Engineering, Liaodong UniversityDandong, China
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Liu, Xiaojian
 e 
Huihong Xu

Huihong Xu

School of Information Engineering, Liaodong UniversityDandong, China
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Xu, Huihong
  
29 set 2025
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Pubblicato online: 29 set 2025
Ricevuto: 14 gen 2025
Accettato: 22 apr 2025
DOI: https://doi.org/10.2478/amns-2025-1096
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© 2025 Tao Dong et al., published by Sciendo.
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Structure of the UNIT
Structure of the UNIT

Figure 2.

Vehicle detection network
Vehicle detection network

Figure 3.

Different methods are performed under the strategy of three
Different methods are performed under the strategy of three

Detection accuracy

Algorithm Da-Faster SA-Da-Faster SMNN-MSFF Ours
White 0.78 0.63 0.64 0.77
Black 0.8 0.52 0.65 0.73
Orange 0.8 0.83 0.75 0.77
Silver grey 0.85 0.27 0.34 0.54
Grass green 0.69 0.8 0.84 0.82
Deep grey 0.73 0.27 0.45 0.54
Scarlet 0.77 0.65 0.49 0.8
Gray 0.17 0.04 0.25 0.12
Red 0.59 0.65 0.46 0.61
Green color 0.76 0.85 0.6 0.75
champagne 0.57 0.17 0.33 0.28
Dark blue 0.68 0.39 0.54 0.55
Blue 0.72 0.58 0.59 0.74
Dark brown 0.45 0.07 0.38 0.27
Brown 0.27 0.38 0.32 0.21
Yellow 0.52 0.66 0.31 0.31
Lemon yellow 0.87 0.96 0.57 0.43
Dark orange 0.61 0.63 0.34 0.99
Dark green 0.37 0.3 0.57 0.09
Salmon 0.27 0.33 0.37 0.38
Earth yellow 0.64 0.47 0.66 0.09
Green 0.61 0.08 0.17 0.73
Pink 0.55 0.7 0.91 0.58
Purple 0.00 0.00 0.22 0.00
Mean accuracy(%) 46.15 47.65 48.69 49.88

Quantitative evaluation results on blended-type dataset

Method Blended-1 Blended-2 Blended-3 Average
AttentGAN 26.09/0.414 22.64/0.446 23.91/0.916 24.21/0.592
DetailNet 27.15/0.893 23.52/0.905 23.78/0.803 24.82/0.867
RESCAN 33.16/0.776 23.49/0.897 28.56/0.987 28.40/0.887
PReNet 34.19/0.929 23.96/0.982 29.88/0.791 29.34/0.901
JORDER-E 34.99/0.828 24.2/0.812 28.23/0.822 29.14/0.821
RCDNet 35.54/0.725 24.49/0.902 29.49/0.761 29.84/0.796
RLNet 35.54/0.873 25.31/0.908 30.61/0.924 30.49/0.902
Pix2pix 24.59/0.73 22.49/0.581 24.53/0.68 23.87/0.664
RadNet - 36.65/0.882 24.27/0.808 30.74/0.708 30.55/0.799
Ours 36.82/0.582 30.02/0.947 30.14/0.888 32.33/0.806
CCN* 33.6/0.735 24.58/0.561 32.91/0.592 30.36/0.629
RadNet -* 36.37/0.931 24.75/0.872 31.34/0.948 30.82/0.917
Ours* 36.39/0.941 27.49/0.913 31.21/0.958 31.70/0.937

Quantitative evaluation results on single-type dataset (rain streak)

Method Rain streak Average
Rain200H Rain200L RS_syn RS_real
AttentGAN 22.98/0.73 28.29/0.885 27.53/0.658 24.42/0.425 25.81/0.675
DetailNet 26.34/0.835 34.41/0.869 30.86/0.686 26.18/0.84 29.45/0.808
RESCAN 26.71/0.114 37.02/0.723 38.64/0.982 26.36/0.938 32.18/0.689
PReNet 28.17/0.869 36.82/0.951 39.37/0.969 25.93/0.998 32.57/0.947
JORDER-E 29.51/0.741 39.29/0.975 40.16/0.737 26.31/0.603 33.82/0.764
RCDNet 30.77/0.775 39.79/0.286 44.16/0.833 27.24/0.991 35.49/0.721
RLNet 29.47/0.787 38.36/0.992 37.06/0.966 26.85/0.755 32.94/0.875
Pix2pix 24.1/0.496 29.78/0.943 28.06/0.815 24.9/0.937 26.71/0.798
CCN 28.98/0.722 37.86/0.864 35.1/0.904 26.81/0.901 32.19/0.848
RadNet - 30.38/0.995 38.74/0.985 39.17/0.891 26.67/0.773 33.74/0.911
Ours 30.23/0.985 38.56/0.855 39.57/0.201 27.69/0.929 34.01/0.743

No detection accuracy

Model Pedestrian Rider Car Bus Waggon Bicycle Motorcycle
YOLOV5 44.5% 59.1% 80.3% 56% 56.9% 46.6% 66.5%
Model of this paper 53.1% 73.5% 80.9% 62.9% 60.3% 66.5% 75.2%

Quantitative assessment results on single-type data sets (raindrops)

Method Raindrop Average
RainDrop RD_syn RD_real
AttentGAN 30.6/0.932 27.26/0.87 21.75/0.669 26.54/0.824
DetailNet 25.02/0.594 28.42/1.262 22.13/0.821 25.19/0.892
RESCAN 25.54/0.953 34.45/0.885 23.03/0.584 27.67/0.807
PReNet 25.6/0.526 34.92/0.8 23.66/0.465 28.06/0.597
JORDER-E 26.62/0.989 35.55/0.832 23.83/0.918 28.67/0.913
RCDNet 26.28/0.887 35.18/0.991 24.36/0.837 28.61/0.905
RLNet 26.6/0.518 33.28/0.921 23.85/0.726 27.91/0.722
Pix2pix 25.55/0.935 25.07/0.493 20.46/0.779 23.69/0.736
CCN 31.49/0.871 33.45/0.815 24.63/0.888 29.86/0.858
RadNet - 24.66/0.945 35.4/0.922 23.69/0.832 27.92/0.900
Ours 24.09/0.475 35.61/0.582 28.25/0.943 29.32/0.667

Data amplification

Data set P R mAP
AtmoGAN Haze 75.6% 54.8% 67.1%
HazeSim 72.8% 49.8% 62.8%
GANHaze 67.3% 45.5% 66%
Fog Traffic 85.5% 58.9% 72.7%

YOLOV5 comparison results

Model P R mAP Parameter quantity GFLOPS
YOLOV5 76.9% 53% 56.5% 45.3M 109.4%
Ours 77.2% 53.8% 66.2% 54.4M 302.1%

Quantitative evaluation results on superimposed-type dataset

Method RDS_syn RDS_real Average
AttentGAN 24.91/0.816 21.03/0.999 22.97/0.908
DetailNet 26.56/0.814 22.43/0.19 24.50/0.502
RESCAN 31.65/0.898 21.66/0.446 26.66/0.672
PReNet 32.79/0.782 22.8/0.893 27.80/0.838
JORDER-E 33.3/0.372 23.09/0.352 28.20/0.362
RCDNet 34.18/0.744 23.33/0.817 28.76/0.781
RLNet 32.29/0.861 23.73/0.581 28.01/0.721
Pix2pix 23.78/0.658 20.16/0.652 21.97/0.655
CCN 32.15/0.98 22.81/0.805 27.48/0.893
RadNet - 34.25/0.981 23.55/0.908 28.90/0.945
Ours 34.07/0.801 27.06/0.807 30.57/0.804

Comparison experiment

Model P R mAP Parameter FPS
Faster R-CNN 50.2% 59.6% 57.9% 62M 15.5
SSD 33.2% 44.3% 38.8% 68.6M 31.3
YOLOV3 75.8% 52.2% 59.5% 61.4M 49.7
YOLOV3_SPP 73.5% 46.6% 54.3% 64.5M 43.8
YOLOV4 69.5% 45.9% 48.8% 63.9M 50.5
YOLOV5X 78.9% 53.7% 57.8% 85.8M 27.2
YOLOV7 75.9% 53.5% 59.4% 34.4M 42.2
YOLOV8 70.1% 51.6% 59.6% 41.8M 34.1
DETR 61.8% 44% 46.7% 31.7M 28.8
YOLO-Z 71.5% 47.7% 52.8% 55.6M 28.2
Ours 77.1% 52.4% 68.2% 53.2M 35.5
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