It well known that vehicle detection is an important component of the field of object detection. However, the environment of vehicle detection is particularly sophisticated in practical processes. It is comparatively difficult to detect vehicles of various scales in traffic scene images, because the vehicles partially obscured by green belts, roadblocks or other vehicles, as well as influence of some low illumination weather. In this paper, we present a model based on Faster R–CNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes. First, we proposed a Retinex-based image adaptive correction algorithm (RIAC) to enhance the traffic images in the dataset to reduce the influence of shadow and illumination, and improve the image quality. Second, in order to improve the feature expression of the backbone network, we conducted Neural Architecture Search (NAS) on the backbone network used for feature extraction of Faster R–CNN to generate the optimal cross-layer connection to extract multi-layer features more effectively. Third, we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets, and improve the robustness of the model for challenging targets such as small scale and severe occlusion. In the implementation of the model, K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model. Our model has been trained and tested on the UN-DETRAC dataset, and the obtained results indicate that our method has art-of-state detection performance.

众所周知，车辆检测是物体检测领域的重要组成部分。然而，车辆检测的环境在实际过程中特别复杂。由于车辆部分被绿化带、路障或其他车辆遮挡，以及一些低照度天气的影响，在交通场景图像中检测各种规模的车辆相对困难。在本文中，我们提出了一种基于 Faster R-CNN 并具有 NAS 优化和特征丰富的模型，以实现交通场景中多尺度车辆目标的有效检测。首先，我们提出了一种基于 Retinex 的图像自适应校正算法（RIAC）来增强数据集中的交通图像，以减少阴影和光照的影响，提高图像质量。第二，为了改善骨干网络的特征表达，我们对Faster R-CNN用于特征提取的骨干网络进行了神经架构搜索（NAS），以生成最优的跨层连接，从而更有效地提取多层特征。第三，我们使用object Feature Enrichment，结合多层特征信息和跨层连接后的最后一层的上下文信息来丰富车辆目标的信息，提高模型对具有挑战性的目标如小规模和严重的闭塞。在模型的实现中，使用K-means聚类算法为我们的数据集选择合适的anchor size，以提高模型的收敛速度。我们的模型已经在 UN-DETRAC 数据集上进行了训练和测试，