###### Abstract
* Instead, cloud computing typically offers flexible resource management for computationally intensive tasks through computational offloading, see, e.g., [16; 17].The need to communicate with far-away cloud computing resources in traditional network infrastructures, however, increases the overall service latency significantly. * One approach to overcome the limitations of mobile processing while providing low latency services is to combine local processing and geographically close cloud services for more computationally expensive processing. While current communication networks infrastructure does not typically allow for in-network computing, new softwarized networks provide this flexibility. Figure 1: Object detection use cases including pedestrians and vehicles detection. (**a**) Pedestrian data set detection by YOLOv2 (image from [9]). (**b**) Object detection on the street (image from [10]). * In this article, we focus on the latency optimization aspects of mobile object detection by combining on-device and in-network computing. Our approach can be applied in 5G and beyond networks (as well as any network that has in-situ computing enabled). In this article, we describe the implementation and performance analysis for a real-time object detection method that incorporates this network softwarization and computing resource provisioning. The current trend to edge computing [18; 19] and network softwarization in general enables the flexible service and application deployment under tight latency constraints, such as the one we consider here. Typically, deployments in softwarized networks include a combination of technologies to fulfill the requirements of real-time use cases: Software-Defined Networking (SDN) [20], Network Function Virtualization (NFV) [21], and Service Function Chaining (SFC) [22]. As the network becomes softwarized, Computing in the Network (COIN) and the Mobile Edge Cloud (MEC) [23] become powerful concepts to combine mobile, local, and far computing resources in a flexible fashion per use-case. Computing in the network will significantly reduce latency and issues that stem from extended packet switching across multiple networks, such as congestion. Virtualized resources can be flexibly deployed at various locations closer to the user, follow the user, and be reallocated in a dynamic fashion. In such a setup, initial pre-processing could be performed at edge nodes and reduce the subsequent nodes' latency requirements for real-time services. This split of overall service processing needs is enabled by the layer-based approach used in object detection neural networks and the ability to split the location of processing by connecting the different layers flexibly over the network. We describe the overall approach in the following Section 2, which contains information about the general on-device or on-server object recognition approach. Additionally, we describe the implementation of a single service function split between an initial service client and the server, noting that multiple splits could be performed as well. We follow with the description of results for a latency-focused performance evaluation in Section 3 and discussion in Section 4 before concluding in Section 5.