Detection And Recognition Of Us Warning Signs On Curves

Traffic sign detection and recognition has been studied in multiple areas including civil and transportation engineering, automated driving, and computer vision. However, previous work has devoted relatively less attention to U.S. signs. Among all types of U.S. signs, warning signs are the most crucial to road safety. In this work, we propose a customized detection and recognition method for U.S. warning signs that does not require training. For detection, preprocessing with a two-layer HSV-B filter and style transfer reduces color bias and a substantial number of false positives. We formulate the recognition process as a template-matching problem in which pre-trained deep networks serve as feature extractors and we use the cosine distance as the distance metric. Best results on selected images from Georgia State Route 2 achieve above 90% precision and recall in detection and 92.6% accuracy in recognition. Further tests on large datasets demonstrate that the proposed method is promising, and it can support transportation agencies in the management of their warning sign assets.