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

Authors

• Yanji Jiang, Liaoning Technical University, Huludao 125105, China
• Jiayu Cui, Liaoning Technical University, Huludao 125105, China
• Hao Dong, Suzhou Automotive Research Institute, Tsinghua University, Suzhou 215131, China
• Daqian Liu, Liaoning Technical University, Huludao 125105, China
• Bowen Fei, Liaoning Technical University, Huludao 125105, China
• Miao Yu, R&D Institute of FAW Group Co. , Ltd. , Changchun 130000, China
• Jinshan Huang, FAW Group Co. , Ltd. , Changchun 130000, China

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.

Recommended Citation

Jiang, Yanji; Cui, Jiayu; Dong, Hao; Liu, Daqian; Fei, Bowen; Yu, Miao; and Huang, Jinshan (2026) "Object Detection Networks and Their Interpretability in Rain, Fog, and Snow Scenarios," Journal of System Simulation: Vol. 38: Iss. 6, Article 12.
DOI: 10.16182/j.issn1004731x.joss.25-0649
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss6/12

First Page

1628

Last Page

1646

CLC

TP391.4

Recommended Citation

Jiang Yanji, Cui Jiayu, Dong Hao, et al. Object Detection Networks and Their Interpretability in Rain, Fog, and Snow Scenarios[J]. Journal of System Simulation, 2026, 38(6): 1628-1646.

DOI

10.16182/j.issn1004731x.joss.25-0649