An Attention Bi-box Regression Network for Traffic Light Detection

摘要

Recently, object detection has made significant progress due to the development of deep learning. Since the traffic lights are extremely small objects, it leads to unsatisfactory performance when directly applying the off-the-shelf methods based on deep convolutional neural networks. To deal with this problem, we propose an improved detection network based on Faster R-CNN framework. By introducing an attention module on the top of the network, the network can focus better on the small object regions. At the same time, the features from shallow layers are leveraged for classification and bounding box regression, in which the features of small objects can be captured better. In addition, we design a two-branch network for detecting the traffic light box and the bulb box at the same time. In this manner, the performance of traffic light detection is improved obviously. Compared with other detection algorithms, our model achieves competitive results on VIVA traffic fight challenge dataset.