Research Of Domain Adaptive Object Detection Algorithm Based On Feature Alignment

Domain adaptive object detection aims to enhance the robustness and transferability of object detection models in domain adaptive scenarios.In practical applications of intelligent driving,intelligent education and other fields,the diversity of image styles and features involved in object detection tasks can have an negative effect on the training and testing of models.Especially when there is a clear domain shift between training and test data,model performance can deteriorate significantly.Feature alignment can be used to obtain domain-invariant feature representations and effectively address this problem.However,determining which features should be aligned and how to align them is a challenging task.To address these issues,this thesis proposes a feature alignment-based domain adaptive object detection algorithm,which focuses on deep learning-based object detection and its domain adaptive problem.Following are the main research contents of this thesis.1.This thesis investigates an adaptive positive and negative sample selection method guided by scale for optimizing general object detection algorithms.To address the problem of poor differentiation in scale-level features,we construct a scale-oriented adaptive positive sample selection strategy to help the network better search for positive samples that are favorable for subsequent prediction on the appropriate scale feature map.In addition,we construct a multi-scale feature fusion module that utilizes attention mechanisms to generate reliable intermediate feature maps to strengthen features at various scales,which improves the performance of the model.2.This thesis investigates a multi-classification re-scoring-based feature alignment method for optimizing domain adaptive object detection algorithms.To address the problem of feature information confusion that needs to be aligned,we construct a categoryaware foreground-background separation module that separates foreground pixels by category,enabling the network to better recognize target pixels.We also construct an Io Uaware adaptive loss weighting module that fully integrates category and position information to better extract domain-invariant feature representations,which improves the performance of domain adaptive object detection.3.This thesis investigates a domain information interaction-based regression space alignment method for optimizing domain adaptive object detection algorithms.To address the challenge of bounding box alignment in domain adaptive detection,we construct a multi-detection head regression network that fully utilizes useful knowledge already learned to achieve better information interaction between the source and target domains.We also construct a bounding box discrete probability regression loss,enabling the network to predict bounding boxes in a more free and flexible way,which improves the performance of domain adaptive object detection.