Traffic signal detection and classification in street views using an attention model

Peter Hall, Yifan Lu, Jiaming Lu, Songhai Zhang

Research output: Contribution to journalArticlepeer-review

73 Citations (SciVal)


Detecting small objects is a challenging task. We focus on a special case: the detection and classification of traffic signals in street views. We present a novel framework that utilizes a visual attention model to make detection more efficient, without loss of accuracy, and which generalizes. The attention model is designed to generate a small set of candidate regions at a suitable scale so that small targets can be better located and classified. In order to evaluate our method in the context of traffic signal detection, we have built a traffic light benchmark with over 15,000 traffic light instances, based on Tencent street view panoramas. We have tested our method both on the dataset we have built and the Tsinghua–Tencent 100K (TT100K) traffic sign benchmark. Experiments show that our method has superior detection performance and is quicker than the general faster RCNN object detection framework on both datasets. It is competitive with state-of-the-art specialist traffic sign detectors on TT100K, but is an order of magnitude faster. To show generality, we tested it on the LISA dataset without tuning, and obtained an average precision in excess of 90%.

Original languageEnglish
Pages (from-to)253-266
Number of pages14
JournalComputational Visual Media
Issue number3
Early online date4 Aug 2018
Publication statusPublished - 1 Sept 2018


  • CNN
  • small object detection
  • traffic light benchmark
  • traffic light detection

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


Dive into the research topics of 'Traffic signal detection and classification in street views using an attention model'. Together they form a unique fingerprint.

Cite this