# Detecting Small Signs from Large Images Source: [Detecting Small Signs from Large Images]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:41:16.530Z Authors: Zibo Meng, Xiaochuan Fan, Xin Chen, Min Chen, Yan Tong Published: 2017-06-26T19:42:33.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Traffic Sign Detection on Tsinghua-Tencent 100K (test)](): Traffic Sign Detection; Tsinghua-Tencent 100K (test); Accuracy (Overall): 0.9. ## Original sources and implementation [Original paper]() [PDF]()