  In the past decade, Convolutional Neural Networks (CNNs) have been
demonstrated successful for object detections. However, the size of network
input is limited by the amount of memory available on GPUs. Moreover,
performance degrades when detecting small objects. To alleviate the memory
usage and improve the performance of detecting small traffic signs, we proposed
an approach for detecting small traffic signs from large images under real
world conditions. In particular, large images are broken into small patches as
input to a Small-Object-Sensitive-CNN (SOS-CNN) modified from a Single Shot
Multibox Detector (SSD) framework with a VGG-16 network as the base network to
produce patch-level object detection results. Scale invariance is achieved by
applying the SOS-CNN on an image pyramid. Then, image-level object detection is
obtained by projecting all the patch-level detection results to the image at
the original scale. Experimental results on a real-world conditioned traffic
sign dataset have demonstrated the effectiveness of the proposed method in
terms of detection accuracy and recall, especially for those with small sizes.
