Skip-layer network with optimization method for domain adaptive detection.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Domain adaptation is crucial for object detection to bridge the gap between training and real-world data.
• Existing methods like DA Faster R-CNN often rely solely on global features, limiting adaptation performance.
• The absence of local features in current approaches hinders effective distribution alignment.

Purpose of the Study:

• To propose a novel domain adaptive object detection method, Skip-Layer Network with Optimization (SLNO).
• To enhance domain adaptation by incorporating multi-level features and optimizing model coefficients.
• To improve the alignment of both image-level and instance-level distributions.

Main Methods:

• Developed a multi-level feature fusion component for the domain classifier.
• Introduced a multi-layer domain adaptation component utilizing skip connections for simultaneous image-level and instance-level alignment.
• Applied the cuckoo search (CS) optimization method to fine-tune SLNO coefficients.

Main Results:

• The proposed SLNO method demonstrated strengthened domain alignment capabilities.
• Evaluated on Cityscapes, Foggy Cityscapes, SIM10K, and KITTI datasets, achieving excellent results.
• Outperformed state-of-the-art object detection methods in domain adaptation tasks.

Conclusions:

• The integrated multi-level features and multi-layer adaptation significantly boost domain alignment.
• SLNO effectively addresses the limitations of global-feature-only methods in domain adaptive object detection.
• The proposed improvements are validated as effective for domain adaptation detection challenges.