Work-in-Progress: Object Detection Acceleration Method by Improving Execution Efficiency of AI Device

Abstract

In recent years, there has been a growing need to perform object detection at the edge. Since the edge environment has tight physical constraints, the efficient use of AI devices is a key challenge to execute object detection at high throughput. In this paper, we propose an object detection acceleration method which uses two types of one-stage detectors in combination. After detecting object candidates by a lightweight detector, the method generates aggregated images by combining the candidate images and executes the second more accurate detector on the aggregated images to improve execution efficiency of AI devices. Our evaluations confirmed that the proposed method can speed up object detection by up to eight times for a license plate detection task with almost no accuracy degradation. We conducted evaluations with a car detection task and a pose estimation task as well and confirmed the broad applicability of the proposed method.