Academic Journal of Computing & Information Science, 2026, 9(5); doi: 10.25236/AJCIS.2026.090510.
Wen Su1, Guifang Guo2
1School of Cybersecurity, Xizang Minzu University, Xianyang, China, 712082
2School of Engineering, Xizang Minzu University, Xianyang, China, 712082
With the explosive growth of global vehicle ownership, traffic accidents caused by adverse weather conditions have become a critical public safety issue worldwide. Vehicle object detection, as the core perception technology of intelligent driver assistance systems (IDAS) and autonomous driving systems, directly determines the safety and reliability of road traffic. This paper systematically reviews the evolution of object detection algorithms from traditional handcrafted feature-based methods to deep learning-driven architectures, including two-stage, single-stage, and Transformer-based frameworks. It focuses on the latest research advances (2025-2026) in vehicle detection under typical low-visibility environments (night, fog, rain, snow) and analyzes the key technical challenges restricting practical deployment. Finally, this review summarizes the limitations of existing methods and prospects future research directions, aiming to provide a comprehensive reference for developing high-precision, lightweight, and all-weather robust vehicle detection algorithms.
Vehicle object detection; Deep learning; Low-visibility environments; Adverse weather; Autonomous driving
Wen Su, Guifang Guo. Advances in Vehicle Object Detection Algorithms for Adverse Weather Conditions. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 5: 83-89. https://doi.org/10.25236/AJCIS.2026.090510.
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