Saliency-guided selective magnification for company logo detection
Fast R-CNN is a well-known approach to object detection which is generally reported to be robust to scale changes. In this paper we examine the influence of scale within the detection pipeline in the case of company logo detection. We demonstrate that Fast R-CNN encounters problems when handling objects which are significantly smaller than the receptive field of the utilized network. In order to overcome these difficulties, we propose a saliency-guided multiscale approach that does not rely on building a full image pyramid. We use the feature representation computed by Fast R-CNN to directly classify large objects while at the same time predicting salient regions which contain small objects with high probability. Only selected regions are magnified and a new feature representation for these enlarged regions is calculated. Feature representations from both scales are used for classification, improving the detection quality of small objects while keeping the computational overhead low. Compared to a naive magnification strategy we are able to retain 79% of the performance gain while only spending 36% of the computation time.