Research on a Traffic Sign Recognition Method under Small Sample Conditions

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 11; p. 5091
• Main Authors: Zhang, Xiao, Zhang, Zhenyu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.05.2023; MDPI
• Subjects: Accuracy; Algorithms; Analysis; feature fusion; Feature maps; few-shot learning; FSOD; Image acquisition; Methods; Neural networks; Semantics; Signs; target detection; Teaching methods; Telecommunication systems; Traffic control; Traffic signs; target detection; FSOD; feature fusion; traffic signs; few-shot learning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23115091

Traffic signs are updated quickly, and there image acquisition and labeling work requires a lot of manpower and material resources, so it is difficult to provide a large number of training samples for high-precision recognition. Aiming at this problem, a traffic sign recognition method based on FSOD (few-shot object learning) is proposed. This method adjusts the backbone network of the original model and introduces dropout, which improves the detection accuracy and reduces the risk of overfitting. Secondly, an RPN (region proposal network) with improved attention mechanism is proposed to generate more accurate target candidate boxes by selectively enhancing some features. Finally, the FPN (feature pyramid network) is introduced for multi-scale feature extraction, and the feature map with higher semantic information but lower resolution is merged with the feature map with higher resolution but weaker semantic information, which further improves the detection accuracy. Compared with the baseline model, the improved algorithm improves the 5-way 3-shot and 5-way 5-shot tasks by 4.27% and 1.64%, respectively. We apply the model structure to the PASCAL VOC dataset. The results show that this method is superior to some current few-shot object detection algorithms.