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Spatial Alignment for Unsupervised Domain Adaptive Single-Stage Object Detection.

Hong Liang¹, Yanqi Tong¹, Qian Zhang¹

¹College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266555, China.

Sensors (Basel, Switzerland)

|May 20, 2022

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This study introduces a selective domain adaptation framework to improve object detection in new domains. The method selectively aligns features, enhancing performance for single-stage detectors without extra data.

Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Domain adaptation is crucial for object detection without costly annotations.
Adversarial learning aligns image distributions but struggles with background interference in object detection.
Existing methods primarily focus on two-stage detectors, leaving single-stage detectors under-explored.

Purpose of the Study:

To develop a domain adaptation framework for single-stage object detectors.
To address the challenge of balancing background and target region alignment.
To improve object detection performance across diverse domains.

Main Methods:

Proposing a selective domain adaptation framework for spatial alignment in single-stage detectors.

Keywords:

domain adaptation object detection transfer learning

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Implementing a mechanism to differentiate and selectively attend to background and target regions.

Achieving domain feature alignment without relying on region proposals.

Main Results:

Demonstrated effectiveness across various domain discrepancies: weather, camera angles, synthetic-to-real, and artistic images.
Significantly improved the performance of single-stage object detectors in new domains.
Showcased good scalability of the proposed framework.

Conclusions:

The selective domain adaptation framework effectively enhances single-stage object detection performance.
The method successfully mitigates the negative impact of background regions during domain adaptation.
This approach offers a scalable solution for transferring object detection models to new domains.