Saliency-guided region proposal network for CNN based object detection

Abstract

Robust sensing of the environment is fundamental for driver assistance systems performing safe maneuvers. While approaches to object detection have experienced tremendous improvements since the introduction and combination of region proposal and convolutional neural networks in one framework, the detection of distant objects occupying just a few pixels in images can be challenging though. The convolutional and pooling layers reduce the image information to feature maps; yet, relevant information may be lost through pooling and convolution for small objects. In order to address this challenge, a new approach to proposing regions is presented that extends the architecture of a region proposal network by incorporating priors to guide the proposals towards regions containing potential target objects. Moreover, inspired by the concept of saliency, a saliency-based prior is chosen to guide the RPN towards important regions in order to make efficient use of differences between objects and background in an unsupervised fashion. This allows the network not only to consider local information provided by the convolutional layers, but also to take into account global information provided by the saliency priors. Experimental results based on a distant vehicle dataset and different configurations including three priors show that the incorporation of saliency-inspired priors into a region proposal network can improve its performance significantly.