Object Detection Networks and Their Interpretability in Rain, Fog, and Snow Scenarios

doi:10.16182/j.issn1004731x.joss.25-0649

Abstract

Abstract:

To address the severe degradation of object detection performance under extreme weather conditions, a detection framework based on the Kolmogorov-Arnold theorem, termed KADet, is proposed. A dynamic Kolmogorov-Arnold Transformer is designed, which leverages learnable nonlinear activation functions to enhance the modeling capability for complex distortions introduced by weather degradation. A Kolmogorov-Arnold spatial-channel network is developed by integrating KAT convolution with spatial-channel convolution to strengthen feature learning of relationships between targets and backgrounds in degraded scenes. An improved loss function is introduced to guide the optimization of the activation functions, and interpretability is analyzed through visualization of their curves. Experimental results demonstrate that KADet achieves higher detection accuracy than existing methods on multiple synthetic and real-world adverse-weather datasets. Visualization of the function curves reveals that the model adopts different response strategies for different types of degradation, validating the effectiveness and interpretability of learnable activation functions in weather-degraded scenarios.

Key words: object detection, visual degradation, Kolmogorov-Arnold network, interpretability, attention mechanism

CLC Number:

TP391.4

Jiang Yanji, Cui Jiayu, Dong Hao, Liu Daqian, Fei Bowen, Yu Miao, Huang Jinshan. Object Detection Networks and Their Interpretability in Rain, Fog, and Snow Scenarios[J]. Journal of System Simulation, 2026, 38(6): 1628-1646.

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数据集	图像	自行车	巴士	汽车	摩托车	人类
VOC-FOG	9 578	836	684	2 453	801	13 519
VOC-Rain	9 578	836	684	2 453	801	13 519
VOC-Snow	9 578	836	684	2 453	801	13 519
VOC-FOG-test	2 129	155	156	857	131	3 527
VOC-Rain-test	2 129	155	156	857	131	3 527
VOC-Snow-test	2 129	155	156	857	131	3 527
FDD	101	17	17	425	9	269
RTTS	4 332	534	1 838	18 413	862	7 950

模型	专攻天气类型	类型	人	自行车	小汽车	摩托车	巴士	平均精度
TogetherNet^[3]	雨、雾、雪	ODAW	83.59	67.60	79.12	71.70	86.27	77.66
FFA-YOLOXs*^[4]	雾	IR+OD	78.30	70.31	69.97	68.80	80.72	73.62
DCP-YOLOXs*^[6]	雾	IR+OD	81.84	70.38	78.63	73.48	84.68	77.80
AOD-YOLOXs*^[7]	雾	IR+OD	67.40	49.19	60.51	55.59	62.07	58.95
RDMNet^[19]	雨、雾、雪	ODAW	83.98	71.18	79.16	73.92	86.19	78.89
YOLOXs^[29]	雨、雾、雪	OD	67.67	83.28	77.75	68.91	81.70	75.86
YOLOXs*	雨、雾、雪	OD	73.09	57.22	69.55	59.83	77.34	67.41
DETR*^[30]	雨、雾、雪	OD	79.74	62.48	70.24	64.16	81.31	71.58
MS-DETR*^[31]	雨、雾、雪	OD	81.41	65.13	78.20	71.08	84.52	76.07
VitDet*^[32]	雨、雾、雪	OD	62.08	30.28	36.70	33.70	54.45	43.44
Semi-YOLOXs*^[33]	雾	IR+OD	81.15	76.94	76.92	72.89	84.88	78.56
RestorNet-YOLOXs*^[34]	雾、雨	IR+OD	78.71	67.15	72.56	71.68	82.36	74.49
DS-Net^[35]	雾	ODAW	72.44	60.47	81.27	53.85	61.43	65.89
IA-YOLO^[36]	雾	ODAW	70.98	61.98	70.98	57.93	61.98	64.77
KADet	雨、雾、雪	ODAW	85.23	69.21	80.86	75.35	88.45	79.82

模型	专攻天气类型	类型	人	自行车	小汽车	摩托车	巴士	mAP
TogetherNet	雨、雾、雪	ODAW	25.39	17.86	56.79	7.14	43.25	30.09
FFA-YOLOXs*	雾	IR+OD	19.18	18.07	50.83	2.38	42.77	26.65
DCP-YOLOXs*	雾	IR+OD	21.57	17.85	55.30	3.57	39.92	27.64
AOD-YOLOXs*	雾	IR+OD	24.54	33.82	56.75	4.76	36.04	31.18
RDMNet	雨、雾、雪	ODAW	25.82	20.31	55.83	2.04	33.60	27.52
YOLOXs	雨、雾、雪	OD	24.37	22.33	55.57	14.29	37.34	30.78
YOLOXs*	雨、雾、雪	OD	21.48	18.84	54.67	1.59	30.33	25.38
DETR*	雨、雾、雪	OD	0.01	0	0.03	0	0	0.01
MS-DETR*	雨、雾、雪	OD	0	0	0.04	0	0	0.01
VitDet*	雨、雾、雪	OD	4.64	0	5.73	0	7.48	3.57
Semi-YOLOXs*	雾	IR+OD	22.39	27.73	56.47	4.76	44.93	31.26
RestorNet-YOLOXs*	雾、雨	IR+OD	23.24	18.81	53.71	2.38	35.70	26.77
DS-Net	雾	ODAW	26.74	20.54	58.16	7.14	36.11	29.74
IA-YOLO	雾	ODAW	16.20	11.76	41.43	4.76	17.55	18.34
KADet	雨、雾、雪	ODAW	27.75	27.01	59.69	14.29	42.20	34.19

模型	专攻天气类型	类型	人	自行车	小汽车	摩托车	巴士	mAP
TogetherNet	雨、雾、雪	ODAW	77.05	42.95	68.80	42.41	28.99	52.04
FFA-YOLOXs*	雾	IR+OD	76.52	48.13	64.31	39.74	23.71	50.48
DCP-YOLOXs*	雾	IR+OD	81.16	51.34	71.13	47.20	31.09	56.38
AOD-YOLOXs*	雾	IR+OD	76.49	43.32	61.03	34.54	22.16	47.51
RDMNet	雨、雾、雪	ODAW	82.25	52.38	71.91	48.98	31.53	57.41
YOLOXs	雨、雾、雪	OD	81.78	56.70	70.23	49.48	31.57	57.95
YOLOXs*	雨、雾、雪	OD	80.28	50.75	68.23	41.89	28.89	54.01
DETR*	雨、雾、雪	OD	60.10	40.22	52.06	31.25	20.03	40.73
MS-DETR*	雨、雾、雪	OD	74.19	19.47	58.07	27.52	19.99	39.85
VitDet*	雨、雾、雪	OD	16.56	1.12	5.24	2.37	0.58	5.17
Semi-YOLOXs*	雾	IR+OD	75.71	46.72	62.74	40.37	24.51	50.01
RestorNet-YOLOXs*	雾、雨	IR+OD	77.48	51.43	60.92	43.12	29.16	52.42
DS-Net	雾	ODAW	68.81	18.02	46.13	15.15	15.44	32.71
IA-YOLO	雾	ODAW	67.25	35.28	41.14	20.97	13.64	35.66
KADet	雨、雾、雪	ODAW	84.42	56.39	76.27	58.14	41.38	63.32

模型	专攻天气类型	类型	人	自行车	小汽车	摩托车	巴士	mAP
TogetherNet	雨、雾、雪	ODAW	83.53	69.83	77.88	74.26	86.41	78.38
RDMNet	雨、雾、雪	ODAW	83.77	72.77	77.86	73.03	86.44	78.77
YOLOXs	雨、雾、雪	OD	80.98	65.88	74.54	70.83	84.35	75.32
YOLOXs*	雨、雾、雪	OD	75.94	63.00	66.92	65.32	73.10	68.86
DETR*	雨、雾、雪	OD	80.24	61.79	70.81	60.51	80.25	70.72
MS-DETR*	雨、雾、雪	OD	81.77	68.93	76.26	69.57	85.78	76.46
VitDet*	雨、雾、雪	OD	61.93	29.97	32.84	36.77	56.16	43.54
RestorNet-YOLOXs*	雾、雨	IR+OD	80.44	68.29	71.68	70.16	82.54	74.62
AirNet-YOLOXs*^[37]	雾、雨	IR+OD	80.06	67.81	70.75	69.87	82.77	74.25
KADet	雨、雾、雪	ODAW	84.32	72.31	78.73	73.43	87.21	79.20