RMOPP: Robust Multi-Objective Post-Processing for Effective Object Detection

TL;DR

RMOPP is a post-processing algorithm that enhances pre-trained object detectors by simultaneously optimizing precision and recall, leveraging Pareto frontiers for near real-time, robust detection performance.

Contribution

It introduces a statistically driven, multi-objective post-processing method that significantly improves object detection accuracy without affecting inference speed.

Findings

Enhanced detection performance on YOLOv2 with minimal speed impact

Effective optimization of precision and recall simultaneously

Demonstrated robustness and practicality on MS-COCO dataset

Abstract

Over the last few decades, many architectures have been developed that harness the power of neural networks to detect objects in near real-time. Training such systems requires substantial time across multiple GPUs and massive labeled training datasets. Although the goal of these systems is generalizability, they are often impractical in real-life applications due to flexibility, robustness, or speed issues. This paper proposes RMOPP: A robust multi-objective post-processing algorithm to boost the performance of fast pre-trained object detectors with a negligible impact on their speed. Specifically, RMOPP is a statistically driven, post-processing algorithm that allows for simultaneous optimization of precision and recall. A unique feature of RMOPP is the Pareto frontier that identifies dominant possible post-processed detectors to optimize for both precision and recall. RMOPP explores the full potential of a pre-trained object detector and is deployable for near real-time predictions. We also provide a compelling test case on YOLOv2 using the MS-COCO dataset.