Liu Yan, Qiu Tiantian, Wang Jingwen, Qi Wenting
School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450066, China.
Entropy (Basel). 2021 Nov 11;23(11):1490. doi: 10.3390/e23111490.
Vehicle detection plays a vital role in the design of Automatic Driving System (ADS), which has achieved remarkable improvements in recent years. However, vehicle detection in night scenes still has considerable challenges for the reason that the vehicle features are not obvious and are easily affected by complex road lighting or lights from vehicles. In this paper, a high-accuracy vehicle detection algorithm is proposed to detect vehicles in night scenes. Firstly, an improved Generative Adversarial Network (GAN), named Attentive GAN, is used to enhance the vehicle features of nighttime images. Then, with the purpose of achieving a higher detection accuracy, a multiple local regression is employed in the regression branch, which predicts multiple bounding box offsets. An improved Region of Interest (RoI) pooling method is used to get distinguishing features in a classification branch based on Faster Region-based Convolutional Neural Network (R-CNN). Cross entropy loss is introduced to improve the accuracy of classification branch. The proposed method is examined with the proposed dataset, which is composed of the selected nighttime images from BDD-100k dataset (Berkeley Diverse Driving Database, including 100,000 images). Compared with a series of state-of-the-art detectors, the experiments demonstrate that the proposed algorithm can effectively contribute to vehicle detection accuracy in nighttime.
车辆检测在自动驾驶系统（ADS）设计中起着至关重要的作用，近年来该系统已取得显著进展。然而，夜间场景中的车辆检测仍面临诸多挑战，因为车辆特征不明显且容易受到复杂道路照明或车辆灯光的影响。本文提出了一种高精度车辆检测算法，用于在夜间场景中检测车辆。首先，使用一种改进的生成对抗网络（GAN），即注意力GAN，来增强夜间图像的车辆特征。然后，为了实现更高的检测精度，在回归分支中采用多元局部回归，该回归分支预测多个边界框偏移量。基于更快的基于区域的卷积神经网络（R-CNN），在分类分支中使用一种改进的感兴趣区域（RoI）池化方法来获取具有区分性的特征。引入交叉熵损失以提高分类分支的准确性。使用所提出的数据集对所提方法进行了检验，该数据集由从BDD-100k数据集（伯克利多样化驾驶数据库，包含100,000张图像）中选取的夜间图像组成。与一系列先进的检测器相比，实验表明所提算法能够有效提高夜间车辆检测的准确率。
