Energy-Efficient Object Detection Using Semantic Decomposition

Abstract

In this brief, we present a new approach to optimize energy efficiency of object detection tasks using semantic decomposition to build a hierarchical classification framework. We observe that certain semantic information like color/texture is common across various images in real-world data sets for object detection applications. We exploit these common semantic features to distinguish the objects of interest from the remaining inputs (nonobjects of interest) in a data set at a lower computational effort. We propose a 2-stage hierarchical classification framework, with increasing levels of complexity, wherein the first stage is trained to recognize the broad representative semantic features relevant to the object of interest. The first stage rejects the input instances that do not have the representative features and passes only the relevant instance to the second stage. Our methodology thus allows us to reject certain information at lower complexity and utilize the full computational effort of a network only on a smaller fraction of inputs resulting in energy efficient detection.

Existing System

Gabor filters, wavelet Transform.

Proposed System

In this paper, we presented a systematic approach to optimize energy efficiency of machine learning classifiers in object detection applications. We use the common semantic features observed across images in real-world data sets to distinguish the objects of interest from the remaining inputs at lower complexity with 2-stage classification. Finally, we would like to note that while our 2-stage framework is composed of simple ANNs, the methodology and the underlying feature-based elimination strategy can be extended to deep learning models for more complex recognition tasks.

Architecture

BLOCK DIAGRAM