Due to its insensitivity to light intensity and the capability to capture multidimensional information, polarimetric imaging technology has been proven to have advantages over traditional intensity-based imaging techniques for object detection tasks in adverse environmental conditions, particularly in road traffic scenarios. Recently, with the rapid development of artificial intelligence technology, deep learning (DL)-powered object detection techniques can further enhance recognition accuracy and algorithm robustness. This improvement is made possible by the ability of DL technology to leverage large datasets and extract deeper levels of target-specific features. However, constructing large-scale polarimetric datasets poses challenges as obtaining polarimetric information requires multiple captures of intensity images. In other words, the workload is several times higher compared to traditional imaging techniques. To address the current scarcity of polarimetric datasets and evaluate the practical performance of various algorithms on polarimetric datasets, this paper proposes a Polarimetric Object Detection Benchmark (PODB) dataset. The PODB provides an integrated quality evaluation framework for DL-based object detection algorithms in complex road scenes by incorporating polarimetric imaging. Besides, we conducted extensive object detection experiments using the PODB, which allowed for a comprehensive validation and performance evaluation of effective benchmark algorithms. Furthermore, a physics-based multi-scale image fusion cascaded object detection neural network model is proposed. By combining the multidimensional information provided by polarized images with an adaptive learning multi-decision object detection neural network model, the recognition accuracy of complex road scenes in adverse weather conditions has been improved by approximately 10%. Additionally, we anticipate that PODB will serve as an effective platform for evaluating and comparing the performance of object detection algorithms, as well as providing researchers with a baseline for future studies in developing new DL-based methods.

中文翻译：

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PODB：基于学习的偏振目标检测基准，适用于恶劣天气条件下的道路场景

由于其对光强度不敏感且能够捕获多维信息，偏振成像技术已被证明在恶劣环境条件下（特别是道路交通场景）的物体检测任务中比传统的基于强度的成像技术具有优势。近年来，随着人工智能技术的快速发展，基于深度学习（DL）的目标检测技术可以进一步提高识别精度和算法鲁棒性。这一改进是通过深度学习技术利用大型数据集并提取更深层次的目标特定特征的能力实现的。然而，构建大规模偏振数据集带来了挑战，因为获取偏振信息需要多次捕获强度图像。换句话说，工作量比传统成像技术高出数倍。为了解决当前极化数据集的稀缺问题并评估各种算法在极化数据集上的实际性能，本文提出了极化目标检测基准（PODB）数据集。 PODB 通过结合偏振成像，为复杂道路场景中基于深度学习的目标检测算法提供了集成的质量评估框架。此外，我们使用 PODB 进行了广泛的目标检测实验，这可以对有效的基准算法进行全面的验证和性能评估。此外，提出了一种基于物理的多尺度图像融合级联目标检测神经网络模型。通过将偏振图像提供的多维信息与自适应学习多决策目标检测神经网络模型相结合，恶劣天气条件下复杂道路场景的识别精度提高了约10%。此外，我们预计 PODB 将成为评估和比较目标检测算法性能的有效平台，并为研究人员开发基于深度学习的新方法的未来研究提供基线。