Traffic Sign Recognition And Research Based On Multi-Scale Feature Fusion

With the development of intelligent transportation technologies such as advanced driver assistance systems and autonomous driving systems,traffic sign detection has become a crucial component,and the accuracy and efficiency of its recognition technology have become hot research topics.In autonomous driving systems,accurate identification and understanding of traffic signs are critical steps in ensuring driving safety,and the accurate and efficient detection of traffic signs can help ensure the safety of drivers,passengers,and pedestrians.However,traffic signs are highly random in terms of size,shape,color,and lighting,making it challenging for traditional computer vision techniques to handle these challenges.This paper aims to explore how to address complex traffic conditions and effectively detect and recognize traffic signs.Traditional recognition methods often only consider a single feature scale,while multi-scale feature fusion can combine multiple scales of features to improve recognition accuracy.This paper proposes a new multi-scale feature fusion method for detecting and recognizing traffic signs,which uses multiple different features.In this method,multiple convolutional layers and pooling layers of different scales are introduced in the convolutional neural network to extract multi-scale features.Then,these features are merged into a high-dimensional feature representation through feature fusion operations,which is used for the final classification.In experiments,this algorithm achieved excellent results in traffic sign detection and recognition,demonstrating the effectiveness of multi-scale feature fusion in traffic sign recognition.To address the problem of class imbalance in the traffic sign dataset,this paper proposes a dataset balancing method,including dataset supplementation strategy design and dataset augmentation strategy design.To further improve the accuracy of traffic sign detection,this paper also introduces an improved attention mechanism module,including an adaptive pooling attention mechanism and an attention module design.The adaptive pooling attention mechanism can adaptively adjust the size and location of the pooling region based on different feature maps to achieve better feature extraction.The attention module design can further enhance the extraction and representation of key features,where the channel attention mechanism enables the network to better learn useful features,while the spatial attention can adaptively adjust the weight of the feature map to allow the network to better focus on critical spatial positions.Experimental results show that the traffic sign detection optimization method combining attention mechanisms in complex environments can effectively improve detection accuracy and robustness,with a precision increase of 4.5% compared to the unimproved algorithm.