Distributed Collaborative Framework for Deep Learning in Object Detection

Abstract

Object detection has gained much attention in recent years because of its ability to localize and classify the objects in videos and images that can be incorporated into many applications. Traditional object detection algorithms need substantial computational resources to build a model. There is an increasing demand for a practical approach to constructing object detection models adapted to the local context, limited computing resources, and application logics while supporting real-time inferencing without degrading accuracy and performance. In this thesis, we proposed a distributed-collaborative framework to build practical object detection models. The framework is based on a novel approach for collaborative group inferencing that is designed with a single-class-single-model mechanism for multiple objects in a distributed manner. For useful grouping, we made use of the intraclass correlation from existing models during inferencing. Results from the case studies with Pascal VOC 2007 and our data collected through Google Street View showed that the proposed model significantly improved performance while making it potentially suitable for customized application building with limited computing resources. A prototype for the proposed model has been built, and a neighborhood application has been demonstrated to validate the proposed work.