Traffic signs contain important traffic information. The traditional traffic sign detection method cannot solve the problem of low detection accuracy caused by the small, occupied area of traffic sign images. Based on this, a traffic sign detection algorithm based on improved YOLOv4 is proposed. Firstly, the 13 × 13 large receptive field detection layer is removed on the YOLOv4 structure, and the 104 × 104 detection layer is added. It obtains more global feature information and improves detection accuracy. The attention mechanism is introduced into the algorithm, that is, the backbone network extracts three feature layers and then adds the scSE module. Make the network focus on the target area and improve the algorithm detection ability. Secondly, in order to speed up the convergence of the network and improve the detection accuracy, a dynamic residual connection is added to the backbone network. It promotes the spread of well-performing signals. And use the decoupled-head detection head to use different branches to calculate classification and positioning tasks. By evaluating the average accuracy of the detection effect of CCTSDB traffic sign data set, mAP reaches 97.68 %, which is 3.78 % higher than YOLOv4. Moreover, network convergence experiments have shown that the improved model converges faster. Compared with other models, the improved model has better detection performance for smaller traffic signs and can better meet the actual needs of high-precision detection.
The urban public transport system comprises various networks representing different transportation modes with complex internal connections. The common single-layer complex network cannot fully describe the interior characteristics of the system. Therefore, this paper proposes a multi-layer network modeling method for the urban public transport system, which couples different traffic networks into multi-layer networks. We also proposed a centrality measurement of node transfer and designed an attack strategy called the multi-metric joint. The simulation experiment using the geographic information data of Chengdu urban public transport shows that the modeling method can completely and accurately describe the natural urban public transport system. And the multi-metric joint attack strategy is more effective than the traditional attack strategy, which will cause more severe damage to the network. This paper provides a new perspective and method for multilayer network modeling and vulnerability analysis of urban public transport systems.
Person reidentification (ReID) is an important issue in the field of image processing and computer vision. Because pedestrian images are often affected by various interference factors, such as occlusion, illumination changes, posture changes, and background changes, extracting discriminative features is an important method to improve the accuracy of ReID. Based on the two existing methods of pose-sensitive embedding and batch feature erasing, a new feature extraction model for person ReID tasks is proposed. The model uses the view information as global features and uses the batch feature erasure method to extract fine-grained features. The mutual complementarity of the two features improves the accuracy of person ReID. In addition, by introducing the attention module, the structure of the complex network becomes concise and the amount of calculation becomes smaller. Through a large number of experiments on three public datasets, it can be seen that the proposed model can effectively deal with the occlusion environment, and it can also obtain competitive results when compared with other state-of-the-art models.
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