In recent years, deep convolutional neural networks (CNNs) have been widely applied and have gained considerable success in object detection (OD). However, most of the CNN-based object detectors have been developed to operate under favorable weather conditions, limiting their ability to accurately detect objects in rainy nighttime (RNT) scenes, thereby resulting in low performance. In this work, we introduce a multilevel knowledge transmission network (MKT-Net) to overcome the challenges of detecting objects with the interference of rain and night. Our proposed model accomplishes this objective by collaborating OD with rain removal (RR) and low-illumination enhancement (LE) tasks. Specifically, the MKT-Net is composed of three main subnetworks that share some shallow layers with each other: an OD subnetwork for performing object classification and localization, an RR subnetwork, and an LE subnetwork for generating clear features. To aggregate and transmit multiscale features generated by the RR and LE subnetworks to the OD subnetwork for boosting detection accuracy, we introduce two feature transmission modules with identical architectures. Extensive evaluation on various datasets has demonstrated the effectiveness of our proposed model, which outperformed competing methods by up to 25.43% and 15.26% in mean average precision on a collected RNT dataset and the published rain in driving dataset, respectively, while maintaining high detection speed.

中文翻译：

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雨夜天气条件下物体检测的多级知识传输


近年来，深度卷积神经网络（CNN）得到了广泛应用，并在目标检测（OD）方面取得了相当大的成功。然而，大多数基于 CNN 的物体检测器都是为了在有利的天气条件下运行而开发的，这限制了它们在雨夜 (RNT) 场景中准确检测物体的能力，从而导致性能较低。在这项工作中，我们引入了多级知识传输网络（MKT-Net）来克服在雨天和夜晚的干扰下检测物体的挑战。我们提出的模型通过将 OD 与除雨 (RR) 和低照度增强 (LE) 任务协作来实现这一目标。具体来说，MKT-Net 由三个主要子网络组成，它们彼此共享一些浅层：用于执行对象分类和定位的 OD 子网络、RR 子网络和用于生成清晰特征的 LE 子网络。为了聚合 RR 和 LE 子网络生成的多尺度特征并将其传输到 OD 子网络以提高检测精度，我们引入了两个具有相同架构的特征传输模块。对各种数据集的广泛评估证明了我们提出的模型的有效性，该模型在收集的 RNT 数据集和已发布的驾驶数据集中的降雨量上的平均精度分别比竞争方法高出 25.43% 和 15.26%，同时保持较高的检测速度。