Sustainable utilization of road assets concerning obscured traffic signs recognition

Traffic sign recognition is crucial for sustainable utilization of road assets. However, on real roads, traffic signs are often obscured, making their recognition challenging. Unfortunately, existing research lacks specific analysis of the impact of traffic sign occlusion and methods to improve recognition in such scenarios. This study quantitatively investigates the relationship between the degree of traffic sign occlusion and recognition accuracy. Moreover, a dedicated deep learning model is proposed, utilizing a multi-scale convolutional stacked input layer, to enhance the recognition of obscured traffic signs. Using the Chinese Traffic Sign Recognition Database, this study analyzes the recognition performance of four traffic sign categories: indication signs, prohibition signs, speed limit signs, and warning signs, under different occlusion levels. Three widely used deep learning methods, Visual Geometry Group, Residual Network, and Dense Convolutional Network, are compared with the dedicated model. Experimental results demonstrate a significant decrease in recognition accuracy when traffic signs are obscured. Importantly, the proposed dedicated model outperforms the other methods, achieving accuracies of 80.95%, 90.50%, and 97.22% in the scenarios of 50% occlusion, 25% occlusion, and no occlusion, respectively. This study holds implications for sustainable utilization of road assets.