A Traffic Sign Classification Method using LiDAR Corrected Intensity and Geometric Feature
IEEE Sensors Journal(2024)
摘要
As an important perception sensor for autonomous vehicles (AV), light detection and ranging (LiDAR) provides 3D-spatial and 1D-intensity information. To boost the ability of traffic sign classification (TSC) using LiDAR, a novel classification method combining corrected intensity and geometric feature was proposed to identify traffic sign patterns. An unequal-interval-division (UID) based intensity frequency histogram (IFH) was advanced to form high quality input feature, thus facilitating the optimization of the back-propagation neutral network (BPNN) classifier for better performance. A series of experiments was conducted, including ablation study and parametric investigations involving point density, sign patterns, and instrument types. Results showed that the combination of geometric and corrected intensity (UID-IFH) feature enhanced the classification performance significantly, with the indicator F1 score achieving 0.812~0.921 at the points density of 0.03~0.91pt/cm
2
. Compared to the state-of-the-art commercial in-vehicle LiDAR with unreliable intensity, the F1 score obtained from the high-stable intensity LiDAR has an obvious improvement. The proposed method is expected to obtain better performance with the advancements in LiDAR technology (e.g. high-density imaging in compact size, accurate intensity of low-cost laser), to serve as an effective approach for decision-making in autonomous vehicles.
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关键词
Geometric feature extraction,Intensity correction,Intensity frequency histogram,Light detection and ranging,Traffic sign classification
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