Expressway section speed can visually reflect the section operation condition, and accurate short time section speed prediction has a wide range of applications in path planning and traffic guidance. However, existing expressway speed prediction data have defects, such as sparse density and incomplete object challenges. Thus, this paper proposes a framework for a combined expressway traffic speed prediction model based on wavelet transform and spatial-temporal graph convolutional network (WSTGCN) of the Electronic Toll Collection (ETC) gantry transaction data. First, the framework pre-processes the ETC gantry transaction data to construct the section speeds. Then wavelet decomposition and single-branch reconstruction are performed on the section speed sequences, and the spatial features are captured by graph convolutional network (GCN) for each reconstructed single-branch sequence, and the temporal features are extracted by connecting the gated recurrent unit (GRU). The experiments use the ETC gantry transaction data of the expressway from Quanzhou to Xiamen. The results indicate that the WSTGCN model makes notable improvements compared to the model of the baseline for different prediction ranges.

Object detection in autonomous driving scenarios has become a popular task in recent years. Due to the high-speed movement of vehicles and the complex changes in the surrounding environment, objects of different scales need to be detected, which places high demands on the performance of the network model. Additionally, different driving devices have varying performance capabilities, and a lightweight model is needed to ensure the stable operation of devices with limited computing power. To address these challenges, we propose a lightweight network called BiGA-YOLO based on YOLOv5. We design the Ghost-Hardswish Conv module to simplify the convolution operations and incorporate spatial coordinate information into feature maps using Coordinate Attention. We also replace the PANet structure with the BiFPN structure to enhance the expression ability of features through different weights during the process of fusing multi-scale feature maps. Finally, we conducted extensive experiments on the KITTI dataset, and our BiGA-YOLO achieved a mAP@0.5 of 92.2% and a mAP@0.5:0.95 of 68.3%. Compared to the baseline model YOLOv5, our proposed model achieved improvements of 1.9% and 4.7% in mAP@0.5 and mAP@0.5:0.95, respectively, while reducing the model size by 15.7% and the computational cost by 16%. The detection speed was also increased by 6.3 FPS. Through analysis and discussion of the experimental results, we demonstrate that our proposed model is superior, achieving a balance between detection accuracy, model size, and detection speed.

As one of the largest Internet of Things systems in the world, China’s expressway electronic toll collection (ETC) generates nearly one billion pieces of transaction data every day, recording the traffic trajectories of almost all vehicles on the expressway, which has great potential application value. However, there are inevitable missed transactions and false transactions in the expressway ETC system, which leads to certain false and missing rates in ETC data. In this work, a dynamic search step SegrDTW algorithm based on an improved DTW algorithm is proposed according to the characteristics of expressway ETC data with origin–destination (OD) data constraints and coupling between the gantry path and the vehicle trajectory. Through constructing the spatial window of segment retrieval, the spatial complexity of the DTW algorithm is effectively reduced, and the efficiency of the abnormal ETC data detection is greatly improved. In real traffic data experiments, the SegrDTW algorithm only needs 3.36 s to measure the abnormal events of a single set of OD path data for 10 days. Compared with the mainstream algorithms, the SegrDTW performs best. Therefore, the proposal provides a feasible method for the abnormal event detection of expressway ETC data in a province and even the whole country.

Object detection in images taken by unmanned aerial vehicles (UAVs) is drawing ever-increasing research interests. Due to the flexibility of UAVs, their shooting altitude often changes rapidly, which results in drastic changes in the scale size of the identified objects. Meanwhile, there are often many small objects obscured from each other in high-altitude photography, and the background of their captured images is also complex and variable. These problems lead to a colossal challenge with object detection in UAV aerial photography images. Inspired by the characteristics of eagles, we propose an Eagle-YOLO detection model to address the above issues. First, according to the structural characteristics of eagle eyes, we integrate the Large Kernel Attention Module (LKAM) to enable the model to find object areas that need to be focused on. Then, in response to the eagle’s characteristic of experiencing dramatic changes in its field of view when swooping down to hunt at high altitudes, we introduce a large-sized feature map with rich information on small objects into the feature fusion network. The feature fusion network adopts a more reasonable weighted Bi-directional Feature Pyramid Network (Bi-FPN). Finally, inspired by the sharp features of eagle eyes, we propose an IoU loss named Eagle-IoU loss. Extensive experiments are performed on the VisDrone2021-DET dataset to compare it with the baseline model YOLOv5x. The experiments showed that Eagle-YOLO outperformed YOLOv5x by 2.86% and 4.23% in terms of the mAP and AP50, respectively, which demonstrates the effectiveness of Eagle-YOLO for object detection in UAV aerial image scenes.