Investigating Long-term Training for Remote Sensing Object Detection

TL;DR

This paper explores the effects of very long training schedules on remote sensing object detection models and introduces Dynamic Backbone Freezing, a method that improves accuracy and reduces computational costs during extended training.

Contribution

The paper proposes Dynamic Backbone Freezing, a novel method with a Freezing Scheduler to optimize backbone training in remote sensing detection models during long-term training.

Findings

Enhanced detection accuracy with long-term training.

Reduced computational costs in extended training.

Seamless integration without additional effort.

Abstract

Recently, numerous methods have achieved impressive performance in remote sensing object detection, relying on convolution or transformer architectures. Such detectors typically have a feature backbone to extract useful features from raw input images. A common practice in current detectors is initializing the backbone with pre-trained weights available online. Fine-tuning the backbone is typically required to generate features suitable for remote-sensing images. While the prolonged training could lead to over-fitting, hindering the extraction of basic visual features, it can enable models to gradually extract deeper insights and richer representations from remote sensing data. Striking a balance between these competing factors is critical for achieving optimal performance. In this study, we aim to investigate the performance and characteristics of remote sensing object detection models under very long training schedules, and propose a novel method named Dynamic Backbone Freezing (DBF) for feature backbone fine-tuning on remote sensing object detection under long-term training. Our method addresses the dilemma of whether the backbone should extract low-level generic features or possess specific knowledge of the remote sensing domain, by introducing a module called 'Freezing Scheduler' to manage the update of backbone features during long-term training dynamically. Extensive experiments on DOTA and DIOR-R show that our approach enables more accurate model learning while substantially reducing computational costs in long-term training. Besides, it can be seamlessly adopted without additional effort due to its straightforward design. The code is available at https://github.com/unique-chan/dbf.