Improved Traffic Sign Recognition Algorithm Based on YOLOV4-Tiny

We propose an improved YOLOV4-tiny traffic sign recognition algorithm for easy deployment on mobile or embedded devices to address the problems of a large number of parameters, low recognition accuracy in complex scenarios. The model uses the YOLOV4-tiny network as the basic framework. First, Octave Convolution is introduced into the backbone network to reduce the redundancy of low-frequency features. Second, the convolutional block attention module is used to strengthen the weights of traffic sign regions and reduce the weights of invalid features. Finally, the Feature Pyramid Network structure is replaced by the Simplified Path Aggregation Network structure in the feature fusion stage to enhance the feature information and further reduce the miss detection rate. The experiment proves that our method outperforms YOLOV4-tiny in recognition accuracy and detection speed on the TT100K dataset, and can easily meet the requirements of traffic sign recognition.

Automated summary

We propose an improved YOLOV4-tiny traffic sign recognition algorithm for easy deployment on mobile or embedded devices to address the problems of a large number of parameters and low recognition accuracy in complex scenarios. The model utilizes Octave Convolution to reduce the redundancy of low-frequency features and employs a convolutional block attention module to strengthen the weights of traffic sign regions and reduce the weights of invalid features. Additionally, the Feature Pyramid Network structure is replaced by the Simplified Path Aggregation Network structure to enhance feature information and reduce the miss detection rate.