Small object detection in aerial view based on improved YoloV3 neural network

Lin, Fuyan; Zheng, Xin; Wu, Qiang

doi:10.1109/aeeca49918.2020.9213538

Cited by 11 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Point of interest (hereinafter referred to as POI) objects such as schools, hospitals, and shopping malls contain abundant human activity characteristics, which reduce symbol recognition errors caused by background information interference. The You Only Look Once Version 3 (hereinafter referred to as YOLOv3) [23,24] algorithm has the advantage of recognizing small targets [25,26] with respect to both real-time performance and accuracy. An attention module can be added to enhance the symbol feature extraction capability for problems such as symbol deformation during map scanning.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Zhou

et al. 2022

IJGI

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%