Machine Recognition of Map Point Symbols Based on YOLOv3 and Automatic Configuration Associated with POI

Abstract

This study is oriented towards machine autonomous mapping and the need to improve the efficiency of map point symbol recognition and configuration. Therefore, an intelligent recognition method for point symbols was developed using the You Only Look Once Version 3 (YOLOv3) algorithm along with the Convolutional Block Attention Module (CBAM). Then, the recognition results of point symbols were associated with the point of interest (POI) to achieve automatic configuration. To quantitatively analyze the recognition effectiveness of this study algorithm and the comparison algorithm for map point symbols, the recall, precision and mean average precision (mAP) were employed as evaluation metrics. The experimental results indicate that the recognition efficiency of point symbols is enhanced compared to the original YOLOv3 algorithm, and that the mAP is increased by 0.55%. Compared to the Single Shot MultiBox Detector (SSD) algorithm and Faster Region-based Convolutional Neural Network (Faster RCNN) algorithm, the precision, recall rate, and mAP all performed well, achieving 97.06%, 99.72% and 99.50%, respectively. On this basis, the recognized point symbols are associated with POI, and the coordinate of point symbols are assigned through keyword matching and enrich their attribute information. This enables automatic configuration of point symbols and achieves a relatively good effect of map configuration.

An Automated Method for Generating Prefabs of AR Map Point Symbols Based on Object Detection Model

“…The YOLOv8 series includes five sub-models: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. Among these, YOLOv8x has the slowest detection speed but the highest mAP (mean average precision) [43]. Therefore, we chose to compare YOLOv8x with the other models.…”