To solve the problem of difficult face detection in a low illumination vehicle environment, a novel multi-scale retinex color restoration (MSRCR) approach exploiting the RGB three-channel decomposition and guided filtering (MSRCR-3CGF) is proposed. The MSRCR algorithm is employed to remove the artifacts and interference of low-light in the image based on the face detector using a multi-task cascaded convolutional neural network (MTCNN). The enhanced face image is decomposed into RGB, and GF is applied to each channel. The proposed method is tested on three widely used datasets: Dark Face, large-scale CelebFaces attributes (CelebA) and WIDER FACE, and an actual low-light scene in vehicles. The experimental results show that the proposed method suppresses the high-frequency noise of MSRCR, whilst improving the image enhancement and accuracy in the face detection in a low-light vehicle environment.

为了解决低照度车辆环境中人脸检测困难的问题，提出了一种利用 RGB 三通道分解和引导滤波 (MSRCR-3CGF) 的新型多尺度视网膜颜色恢复 (MSRCR) 方法。MSRCR算法用于基于人脸检测器使用多任务级联卷积神经网络（MTCNN）去除图像中低光的伪影和干扰。增强后的人脸图像被分解为RGB，对每个通道应用GF。所提出的方法在三个广泛使用的数据集上进行了测试：Dark Face、大规模 CelebFaces 属性 (CelebA) 和 WIDER FACE，以及​​车辆中的实际低光场景。实验结果表明，该方法抑制了MSRCR的高频噪声，