The effective use of context information can significantly improve the effect of object detection. This paper proposes a method to exploit the context of a hard example searched by online hard example mining for improving the detection effect for people in crowded scenes. As shown by these experiments, this method can improve the effectiveness of faster R-CNN networks on people detection with a smaller convolutional neural network model. Because smaller convolutional neural network model is used, both the running memory consumption and the computing time can be reduced. Hence, our method can be implemented more easily on embedded devices.
Fully convolutional networks (FCNs) have shown outstanding performance in image semantic segmentation, which is the key work in license plate detection (LPD). An FCN architecture for LPD is presented. First, a multiscale hierarchical network structure is used to combine multiscale and multilevel features produced by FCN. Then, an enhanced loss structure that contains three loss layers is defined to emphasize the license plates in images. Finally, the FCN generates prediction maps that directly show the location of license plates. Experiments show that our approach is more accurate than many state-of-the-art methods.