Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics

TL;DR

This paper introduces the CURE-TSD-Real dataset for evaluating traffic sign detection under challenging conditions, analyzes performance degradation, and explores spectral features as indicators of detection difficulty.

Contribution

The paper presents a new dataset simulating real-world challenging conditions and investigates the impact on detection performance and spectral characteristics.

Findings

Severe conditions cause up to 29% drop in precision and 68% in recall.

Spectral analysis reveals distinct magnitude spectrum features under challenging conditions.

Mean magnitude spectrum can serve as an indicator of detection performance.

Abstract

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 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 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. CURE-TSD-Real dataset is available online at https://github.com/olivesgatech/CURE-TSD.