Abstract: Bots are intelligent software that can interact with people,and usually have the characteristics of real-time and interactivity.This paper takes the bots driven by visual context awareness as the theme,and explores from four aspects:lightweight target detection model and compression,real-time key frame extraction,system optimization,and interaction strategy,and builds strong real-time on edge resource-constrained devices.A flexible,highly interactive and highly scalable bots system.Specifically,in terms of lightweight target detection models and compression,we first explore the performance and accuracy of different lightweight target detection models,and compress the SSD model based on the VGG16 network to find a suitable compression strategy.Compression on the latest SSD model can increase the frame rate by 187% compared with the original model,under the pre-mise that the accuracy loss does not exceed 0.1%.In terms of real-time key frame extraction,the input video stream is pre-screened to reduce system pressure,which is equivalent to reducing inference delay by 90%.In terms of system optimization,the use of microservices reduces the cold start delay by about 98%.In terms of interaction strategy,a state machine with timer is used to model the situation to achieve situation-driven,and the output of human-computer interaction is completed in the form of speech.

Key words: Resource-constrained, Lightweight model, Model compression, Object detection, Context-driven