Traffic Sign Detection Under Challenging Conditions: A Deeper Look into Performance Variations and Spectral Characteristics

• Published in: IEEE transactions on intelligent transportation systems 2020-09, Vol.21 (9), p.3663-3673
• Main Authors: Temel, Dogancan, Chen, Min-Hung, AlRegib, Ghassan
• Format: Article
• Language: English
• Subjects: Algorithms; Autonomous vehicles; Cameras; challenging conditions dataset; Computer simulation; Datasets; Detection algorithms; Image color analysis; Intelligent transportation systems; Lenses; machine learning; magnitude spectrum; Metadata; Performance degradation; Spectrum analysis; Traffic control; Traffic safety; traffic sign detection and recognition; Traffic signs; Video sequences
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2019.2931429

Traffic signs are critical for maintaining the safety and efficiency of our roads. Therefore, we need to carefully assess the capabilities and limitations of automated traffic sign detection systems. Existing traffic sign datasets are limited in terms of type and severity of challenging conditions. Metadata corresponding to these conditions are unavailable and it is not possible to investigate the effect of a single factor because of the simultaneous changes in numerous conditions. To overcome the shortcomings in existing datasets, we introduced the CURE-TSD-Real dataset, which is based on simulated challenging conditions that correspond to adversaries that can occur in real-world environments and systems. We test the performance of two benchmark algorithms and show that severe conditions can result in an average performance degradation of 29% in precision and 68% in recall. We investigate the effect of challenging conditions through spectral analysis and show that the challenging conditions can lead to distinct magnitude spectrum characteristics. Moreover, we show that mean magnitude spectrum of changes in video sequences under challenging conditions can be an indicator of detection performance. The CURE-TSD-Real dataset is available online at https://github.com/olivesgatech/CURE-TSD .