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

• URL: http://arxiv.org/abs/2102.04582v1
• Date: Tue, 9 Feb 2021 00:02:38 GMT
• Title: RMOPP: Robust Multi-Objective Post-Processing for Effective Object Detection
• Authors: Mayuresh Savargaonkar, Abdallah Chehade and Samir Rawashdeh
• Abstract summary: RMOPP is a statistically driven, post-processing algorithm that allows for simultaneous optimization of precision and recall. We provide a compelling test case on YOLOv2 using the MS-COCO dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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