Vehicle Face Detection Based on Cascaded Convolutional Neural Networks
Shen Jing, Changhui Hu, Cailing Wang, Guangliang Zhou, Jian Feng Yu · 2019
Vehicle face detection is an important and challenging task in face detection. On one hand, the face image is affected by the complex environment during driving so that the facial details are easily lost. On the other hand, there is no large public vehicle face dataset. In order to solve these problems, firstly, inspired by the convolutional neural network, an improved method based on cascaded convolutional neural networks is proposed in this paper. The cascaded convolutional networks consist of three lightened convolutional neural networks. Each convolutional neural network uses multi-task learning. Feature fusion technology focusing on learning details of vehicle face image is also adopted in cascaded convolutional networks. Secondly, due to the lack of vehicle face image samples, we build a small vehicle face dataset by ourselves, named Driver FACE dataset. Experiments on Driver FACE dataset show that our method improves the performance of vehicle face detection compared with baseline.