Abstract
Abstract. This paper presents a traffic sign detection and recognition method from mobile LiDAR data and digital images for intelligent transportation-related applications. The traffic sign detection and recognition method includes two steps: traffic sign interest regions are first extracted from mobile LiDRA data. Next, traffic signs are identified from digital images simultaneously collected from the multi-sensor mobile LiDAR systems via a convolutional capsule network model. The experimental results demonstrate that the proposed method obtains a promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.
Highlights
Traffic signs play an important role in road transportation systems because they provide useful and vital road information and instruction to drivers and road users (Gudigar and Chokkadi, 2014)
Traffic sign detection and recognition (TSDR) have been developed and employed for recent years (Salti et al, 2014; Jin et al, 2014; Liu et al, 2014), manually investigating traffic signs is still a popular way in traffic sign inventory and monitoring
Video-based and image-based traffic sign detection and recognition (TSDR) systems suffer from the following limitations: 1) weather conditions, affecting the visibility of traffic signs, 2) shadows, caused by other adjacent objects or different illumination levels, 3) traffic signs with bad placement or disorientation, which is relevant to the usability and viability of traffic signs, and affects the road safety of road users, and 4) variable color and shape information of traffic signs
Summary
Traffic signs play an important role in road transportation systems because they provide useful and vital road information and instruction to drivers and road users (Gudigar and Chokkadi, 2014). Traffic signs usually exhibit high retro-reflectivity (in a form of intensity) in the MLS point clouds Such intensity information becomes an important clue for distinguishing traffic signs from other pole-like objects (Wen et al, 2015). Deep learning methods, such as deep neural networks (DNN) ( Arcos-García et al, 2017), and capsule convolutional networks (Sabour et al, 2018) can automatically abstract high-level feature representations from voluminous data samples, which have become attractive in traffic sign recognition. These deep learning methods are proven to generate superior experimental results. Different from classical CNN models that take into account only the probability, capsule networks are more powerful and robust to abstract intrinsic features of objects
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