Detecting Small Signs from Large Images

TL;DR

This paper introduces a method for detecting small traffic signs in large images by breaking images into patches and using a modified SSD framework, improving detection accuracy for small objects under real-world conditions.

Contribution

The paper proposes a novel patch-based detection approach with a Small-Object-Sensitive-CNN to enhance small object detection in large images, addressing memory and performance issues.

Findings

Improved detection accuracy for small traffic signs.

Enhanced recall rates for small objects.

Effective detection in real-world traffic sign datasets.

Abstract

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.