Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and efficiency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm of ship target detection is proposed. In the proposed method, the image downscaling method is used to enhance the useful information of the ship image. The scene narrowing technique is used to construct the target regional positioning network and the Faster R-CNN convolutional neural network into a hierarchical narrowing network, aiming at reducing the target detection search scale and improving the computational speed of Faster R-CNN. Furthermore, deep cooperation between main network and subnet is realized to optimize network parameters after researching Faster R-CNN with subject narrowing function and selecting texture features and spatial difference features as narrowed sub-networks. The experimental results show that the proposed method can significantly shorten the detection time of the algorithm while improving the detection accuracy of Faster R-CNN algorithm.

In the digital economy context, enterprises’ competitive environment is changing rapidly. Historically, enterprises rely on a solitary fight to occupy the market. Now, enterprises should actively embed into digital technology innovation networks to maximize access to external digital technology knowledge resources through organizational cooperation and achieve the absorption of digital resources and technologies. However, the relationship between digital technology innovation network embedding and innovation performance still needs to be clarified. Therefore, this study adopts the “structure–behavior–performance” research paradigm to extend innovation network research to the digital technology innovation network context, aiming to explore the impact of digital technology innovation network embedding on enterprise innovation performance and to analyze the mediating effect of knowledge acquisition and the moderating effect of digital transformation. This study conducts an empirical study based on Chinese A-share listed firms that undertook digital technology innovation from 2010–2021. The findings show that digital technology innovation networks’ relational and structural embedding positively affects firm innovation performance. Knowledge acquisition mediates digital technology innovation network embedding and innovation performance. Digital transformation has a moderating role between digital technology innovation network embedding and innovation performance, and different levels of digital transformation will have different effects on firms’ innovation performance. Overall, the relational and structural embedding of digital technology innovation networks can encourage enterprises to acquire more social capital and tacit knowledge and reduce R&D costs, thus improving their innovation performance. Firms should focus on building external cooperation networks, actively establishing an excellent corporate image, strengthening communication and cooperation with network members, establishing mutually beneficial cooperation beliefs, and promoting digital transformation. The present results will help companies understand the impact of digital technology innovation networks and provide a reference for companies to utilize in digital transformation to improve their innovation performance.

No abstract

No abstract

No abstract