Identification and detection of traffic identifiers are some of the most challenging tasks in the field of autonomous driving. For object detection, how to obtain deep semantic information and shallow spatial information has always been a permanent subject. This paper proposes a dense network structure for the detection of traffic identifiers; the deep structure of the residual network is used to obtain the high-level semantic information of the detection target; the regression operation is used to obtain the underlying spatial information; the detection structure is higher than the previous The accuracy of the network structure and the generalization ability of the network has been improved; ASPP using the attention mechanism; the main function of ASPP is to obtain a continuous feature map; using the ASPP of the attention mechanism, the prior balance between the receptive field and the feature resolution map is solved; In the paper, a collection of residual networks is used to obtain a variety of acceptance domains; through a regression operation, an effective interactive connection between the receptive field and the acceptance domain is obtained; a deep network using regression residuals allows geometric transformation of the model during data training The ability to improve has been improved; at the same time, the use of a dense network structure has realized the diversity of different receptive fields in the process of multi-scale feature extraction.

Human key-point detection is a challenging research field in computer vision. Convolutional neural models limit the number of parameters and mine the local structure, and have made great progress in significant target detection and key-point detection. However, the features extracted by shallow layers mainly contain a lack of semantic information, while the features extracted by deep layers contain rich semantic information but a lack of spatial information that results in information imbalance and feature extraction imbalance. With the complexity of the network structure and the increasing amount of computation, the balance between the time of communication and the time of calculation highlights the importance. Based on the improvement of hardware equipment, network operation time is greatly improved by optimizing the network structure and data operation methods. However, as the network structure becomes deeper and deeper, the communication consumption between networks also increases, and network computing capacity is optimized. In addition, communication overhead is also the focus of recent attention. We propose a novel network structure PGNet, which contains three parts: pipeline guidance strategy (PGS); Cross-Distance-IoU Loss (CIoU); and Cascaded Fusion Feature Model (CFFM).

The indagation improves the SSD network to improve the target detection performance in unmanned driving at night. In target detection, the goal is to identify and locate the different types of objects present in an image. The first-level target detection method pulls categorization information and target position information for use by the second-level target detection algorithm using the featured MAP created by the deep network. The depth characteristics, on the other hand, are processed using long-distance convolution and downsampling. Given the lack of geographical information, research alludes to the concept of semantic segmentation and proposes a method for improving the first-level target identification algorithm SSD by mixing shallow characteristics from the backbone network with deep features obtained through sampling. In addition, this research enhances the generation method and loss function of the preselection box by taking into account the peculiarities of pedestrian detection data. Undertakings to experiments on the two data sets provided by INRIA and Caltech show that the improved model USSD reported in this paper improves both the efficiency of detection and speed of retrieval.

The difficulty of traffic flow prediction in intelligent transportation system is its non-linearity and correlation, There are many factors affecting the short-term traffic flow, but the prediction of the traffic flow at the next moment is closely related to the information of the historical moment.In this paper, a prediction model based on deep cyclic network (RNN) is proposed. The feature data are extracted from the data of the relevant time as the reference standard to predict the traffic at the future time. The simulation results show that the prediction effect of this algorithm is obviously improved.

With the double wave of the new industrial revolution and the new economic revolution, traditional engineering education faces unprecedented challenges. The traditional engineering training model and the cultivated talents cannot adapt well to the needs of the industrial revolution under the new situation. Under the background of new engineering construction, the development of local applied universities has ushered in tremendous development opportunities. This paper studies the talents training mode of local applied universities, and proposes the integration of deep production and education, enterprise modular practice training, and multi-faceted training mode. reform. “Reform and Development Education First," China’s engineering education must seize the opportunities of new industry development and new technology innovation, take into account the recent and long-term development needs of the engineering field, and vigorously promote the reform of the talent training model, thereby promoting the national and regional human capital structure. The upgrade will help transform the economic model from a traditional economy to a new economy.