In recent years, significant progress has been witnessed in the field of deep learning-based object detection. As a subtask in the field of object detection, traffic sign detection has great potential for development. However, the existing object detection methods for traffic sign detection in real-world scenes are plagued by issues such as the omission of small objects and low detection accuracies. To address these issues, a traffic sign detection model named YOLOv7-Traffic Sign (YOLOv7-TS) is proposed based on sub-pixel convolution and feature fusion. Firstly, the up-sampling capability of the sub-pixel convolution integrating channel dimension is harnessed and a Feature Map Extraction Module (FMEM) is devised to mitigate the channel information loss. Furthermore, a Multi-feature Interactive Fusion Network (MIFNet) is constructed to facilitate enhanced information interaction among all feature layers, improving the feature fusion effectiveness and strengthening the perception ability of small objects. Moreover, a Deep Feature Enhancement Module (DFEM) is established to accelerate the pooling process while enriching the highest-layer feature. YOLOv7-TS is evaluated on two traffic sign datasets, namely CCTSDB2021 and TT100K. Compared with YOLOv7, YOLOv7-TS, with a smaller number of parameters, achieves a significant enhancement of 3.63% and 2.68% in the mean Average Precision (mAP) for each respective dataset, proving the effectiveness of the proposed model.

Currently, many real-time semantic segmentation networks aim for heightened accuracy, inevitably leading to increased computational complexity and reduced inference speed. Therefore, striking a balance between accuracy and speed has emerged as a crucial concern in this domain. To address these challenges, this study proposes a dual-branch fusion network with multiscale atrous pyramid pooling aggregate contextual features for real-time semantic segmentation (MAFNet). The first key component, the semantics guide spatial-details module (SGSDM) not only facilitates precise boundary extraction and fine-grained classification, but also provides semantic-based feature representation, thereby enhancing support for spatial analysis and decision boundaries. The second component, the multiscale atrous pyramid pooling module (MSAPPM), is designed by combining dilation convolution with feature pyramid pooling operations at various dilation rates. This design not only expands the receptive field, but also aggregates rich contextual information more effectively. To further improve the fusion of feature information generated by the dual-branch, a bilateral fusion module (BFM) is introduced. This module employs cross-fusion by calculating weights generated by the dual-branch to balance the weight relationship between the dual branches, thereby achieving effective feature information fusion. To validate the effectiveness of the proposed network, experiments are conducted on a single A100 GPU. MAFNet achieves a mean intersection over union (mIoU) of 77.4% at 70.9 FPS on the Cityscapes test dataset and 77.6% mIoU at 192.5 FPS on the CamVid test dataset. The experimental results conclusively demonstrated that MAFNet effectively strikes a balance between accuracy and speed.

Mobile edge computing extends the capabilities of the cloud to the edge to meet the latency performance required by new types of applications. Task caching reduces network energy consumption by caching task applications and associated databases in advance on edge devices. However, determining an effective caching strategy is crucial since users generate numerous repetitive tasks, but edge devices and storage resources are limited. We aimed to address the problem of highly coupled decision variables in dynamic task caching and computational offloading for multiuser multitasking in mobile edge computing systems. This paper presents a joint computation and caching framework with the aim of minimizing delays and energy expenditure for mobile users and transforming the problem into a form of reinforcement learning. Based on this, an improved deep reinforcement learning algorithm, P-DDPG, is proposed to achieve efficient computation offloading and task caching decisions for mobile users. The algorithm integrates a deep and deterministic policy grading and a prioritized empirical replay mechanism to reduce system costs. The simulations show that the designed algorithm performs better in terms of task latencies and lower computing power consumption.

In the field of semantic segmentation, the limited receptive field of convolutional neural networks leads to insufficient extraction of global features, thereby affecting the accuracy of network segmentation. To address this issue, a Hierarchical Hybrid Encoder Network (HHEnet) based on Transformers is proposed for semantic segmentation. Firstly, to solve the problem of limited global feature information caused by the network’s limited receptive field, a Hierarchical Hybrid Encoder (HHE) is introduced, which consists of a Hierarchical Convolutional Encoder (HCE) and a Hierarchical Transformer Encoder (HTE). The encoder combines the advantages of convolution and transformers, allowing for effective extraction of both shallow and deep features. In order to further enhance spatial and global semantic information, the Feature Enhancement Module (FEM) was introduced, which consisted of two feature enhancement modules: spatial feature enhancement module (SEM) and global feature enhancement module (GEM), which enhanced spatial detail information and global semantic information respectively. Thus the accuracy of semantic segmentation can be improved. Finally, to alleviate the discrepancy between the features of the convolutional encoder and the transformer encoder, a Feature Guidance Module (FGM) is introduced. Experimental results conducted on Cityscapes, ADE20K and PASCAL VOC2012 datasets achieved mIoU scores of up to 81.9%, 49.4% and 79.1%, respectively. Compared to state-of-the-art networks, the research results confirm the higher segmentation accuracy of the proposed HHEnet in this study.

Most real-time semantic segmentation networks use shallow architectures to achieve fast inference speeds. This approach, however, limits a network’s receptive field. Concurrently, feature information extraction is restricted to a single scale, which reduces the network’s ability to generalize and maintain robustness. Furthermore, loss of image spatial details negatively impacts segmentation accuracy. To address these limitations, this paper proposes a Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network (BMSeNet). First, to address the limitation of singular semantic feature scales, a Multiscale Context Pyramid Pooling Module (MSCPPM) is introduced. By leveraging various pooling operations, this module efficiently enlarges the receptive field and better aggregates multiscale contextual information. Moreover, a Spatial Detail Enhancement Module (SDEM) is designed, to effectively compensate for lost spatial detail information and significantly enhance the perception of spatial details. Finally, a Bilateral Attention Fusion Module (BAFM) is proposed. This module leverages pixel positional correlations to guide the network in assigning appropriate weights to the features extracted from the two branches, effectively merging the feature information of both branches. Extensive experiments were conducted on the Cityscapes and CamVid datasets. Experimental results show that the proposed BMSeNet achieves a good balance between inference speed and segmentation accuracy, outperforming some state-of-the-art real-time semantic segmentation methods.