QU C Y,CHENG Y Y. Traffic sign detection algorithm based on improved YOLOv7[J]. Microelectronics & Computer,2024,41(7):8-17. doi: 10.19304/J.ISSN1000-7180.2023.0399
Citation: QU C Y,CHENG Y Y. Traffic sign detection algorithm based on improved YOLOv7[J]. Microelectronics & Computer,2024,41(7):8-17. doi: 10.19304/J.ISSN1000-7180.2023.0399

Traffic sign detection algorithm based on improved YOLOv7

  • With the rapid development of intelligent driving system, traffic sign detection technology has been widely concerned. Aiming at the problems of small pixel area, low resolution and complex background of traffic signs in images, an improved traffic sign detection algorithm based on YOLOv7 is proposed. Firstly, an enhanced feature extraction module is constructed, which uses the residual bottleneck structure and the full-dimensional dynamic convolution layer to optimize the extendable efficient layer aggregation network structure in the feature extraction network, so as to improve the ability of the feature extraction network to focus on the key features of small-target traffic signs and avoid feature loss. Secondly, a lightweight hybrid attention module is embedded in the feature fusion network to filter the complex background noise around small target traffic signs, so that the neck of the network can better integrate the shallow detail information and deep semantic information, and enhance the effect of multi-scale feature fusion. Finally, the network detection head is decouped, and two independent branches with different parameters are used to complete the classification and regression tasks of small target traffic signs respectively, which improves the accuracy of classification and regression. Experimental evaluation is conducted using the TT100K traffic sign detection data set. The results show that compared with baseline YOLOv7 algorithm, the accuracy of small target is increased by 1.9%, the recall rate of small target is increased by 3.1%, and the mAP value is increased by 2.6%. Additionally, the improved algorithm achieves a detection speed of 57.1 frames per second, meeting the requirements of real-time detection.
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