Safety helmet wearing plays a major role in protecting the safety of workers in industry and construction, so a real-time helmet wearing detection technology is very necessary. This paper proposes an improved YOLOv4 algorithm to achieve real-time and efficient safety helmet wearing detection. The improved YOLOv4 algorithm adopts a lightweight network PP-LCNet as the backbone network and uses deepwise separable convolution to decrease the model parameters. Besides, the coordinate attention mechanism module is embedded in the three output feature layers of the backbone network to enhance the feature information, and an improved feature fusion structure is designed to fuse the target information. In terms of the loss function, we use a new SIoU loss function that fuses directional information to increase detection precision. The experimental findings demonstrate that the improved YOLOv4 algorithm achieves an accuracy of 92.98%, a model size of 41.88 M, and a detection speed of 43.23 pictures/s. Compared with the original YOLOv4, the accuracy increases by 0.52%, the model size decreases by about 83%, and the detection speed increases by 88%. Compared with other existing methods, it performs better in terms of precision and speed.

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Edge computing is a creative computing paradigm that enhances the computing capacity of the edge device close to the data source. As the key technology of edge computing, task offloading, which can improve the response speed and the stability of the network system, has attracted much attention and has been applied in many network scenarios. However, few studies have considered the application of task offloading in time-sensitive networking (TSN), which is a promising technology that has the potential to guarantee data delivery with bounded latency and low jitter. To this end, we establish a task offloading stream transmission model for TSN based on the queueing theory. With the model, the average response time can be achieved by quantitative calculation. Then, we introduce the backward method to construct a utility function and formulate an exact potential game to model the task offloading competition among edge devices considering the minimization of the average response time of all tasks. Furthermore, a distributed and sequential decision-making algorithm for multitask offloading (DSDA-MO) is proposed to find the Nash equilibrium. Through numerical studies, we evaluate the algorithm performance as well as the benefit of the multitask offloading mechanism. The results reveal that through the proposed game theoretic approach, we can obtain the optimal multitask offloading strategy, which can significantly reduce the task computation delay in TSN, within a finite number of rounds of calculation.

Agile Product Line Engineering (APLE), a relatively new approach combining the two successful methods of Agile Software Development (ASD) and Software Product Lines (SPLs), makes product lines more responsive to ever-changing customer needs or market changes. However, SPLs often fail to keep up with market demand due to high coordination costs, slow development processes, and long release cycles in the case of frequent changes in business requirements; in agile software projects, the lack of a unified specification for describing requirements leads to high coordination costs and inconvenient requirement management. Some studies in the literature have proposed optimized approaches to integrate ASD and SPLs, but they still have not covered all aspects of APLE’s characteristics, and software resource reuse is rarely considered in these approaches during product line development. In view of this, we propose a collaborative framework of agile product line engineering for software resource reuse, namely ScrumOntoSPL. The ScrumOntoSPL approach efficiently merges ASD and SPL based on the agile method Scrum, SPL architecture, and ontology technology. In ScrumOntoSPL, uniform requirement specification is constructed by utilizing ontology, and the Matching Requirement with Component (MRC) process is designed to match product new requirements and software resources stored in a resource pool. In addition, we evaluated the proposed framework and approach with CMMI. In the end, a case study of a software development tool called IMC-Tool based on ScrumOntoSPL for a universal Instrument Microcontroller Chip (IMC) is discussed. The IMC-Tool case illustrates that the ScrumOntoSPL has the advantages of dynamically managing demand changes, enhancing software resource reuse, reducing coordination costs, and reducing time to market.