无需去雾模块，模型在雾霾环境下仍能精准识别车位占用状态。
Atmospheric Noise-Resilient Image Classification in a Real-World Scenario: Using Hybrid CNN and Pin-GTSVM
- 结合预训练特征提取器与改进的Pin-GTSVM分类器，直接处理雾霾干扰。
- 在CNRPark、PKLot及自建雾霾数据集上准确率显著提升。
- 适合部署于现有智能停车场系统，仅用少量摄像头管理上百车位。
近年来，基于深度学习的停车位占用检测技术取得了显著进展。尽管这些方法能有效识别部分遮挡并适应光照变化，但在存在雾霾时性能明显下降。本文提出一种新型混合模型，采用预训练特征提取器与改进的分位数广义双支持向量机（Pin-GTSVM）分类器，无需依赖去雾系统，对各类大气噪声具有强鲁棒性。该系统可无缝集成至传统智能停车基础设施中，仅需少量摄像头即可高效监控和管理数百个停车位。在CNRPark Patches、PKLot以及专为雾霾场景构建的自定义数据集上进行评估，实验结果表明，在雾霾条件下分类准确率有显著提升，充分验证了其在复杂天气下的高效噪声处理能力。
原文摘要 · Abstract (English)
Parking space occupation detection using deep learning frameworks has seen significant advancements over the past few years. While these approaches effectively detect partial obstructions and adapt to varying lighting conditions, their performance significantly diminishes when haze is present. This paper proposes a novel hybrid model with a pre-trained feature extractor and a Pinball Generalized Twin Support Vector Machine (Pin-GTSVM) classifier, which removes the need for a dehazing system from the current State-of-The-Art hazy parking slot classification systems and is also insensitive to any atmospheric noise. The proposed system can seamlessly integrate with conventional smart parking infrastructures, leveraging a minimal number of cameras to monitor and manage hundreds of parking spaces efficiently. Its effectiveness has been evaluated against established parking space detection methods using the CNRPark Patches, PKLot, and a custom dataset specific to hazy parking scenarios. Furthermore, empirical results indicate a significant improvement in accuracy on a hazy parking system, thus emphasizing efficient atmospheric noise handling.
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