光谱成像技术研究室知识库

Modern detectors are mostly trained under single and limited conditions. However, object detection faces various complex and open situations in autonomous driving, especially in urban street scenes with dense objects and complex backgrounds. Due to the shift in data distribution, modern detectors cannot perform well in actual urban environments. Using domain adaptation to improve detection performance is one of the key methods to extend object detection from limited situations to open situations. To this end, this article proposes a Domain Adaptation of Anchor-Free object detection (DAAF) for urban traffic. DAAF is a cross-domain object detection method that performs feature alignment including two aspects. On the one hand, we designed a fully convolutional adversarial training method for global feature alignment at the image level. Meanwhile, images can generally be decomposed into structural information and texture information. In urban street scenes, the structural information of images is generally similar. The main difference between the source domain and the target domain is texture information. Therefore, during global feature alignment, this paper proposes a method called texture information limitation (TIL). On the other hand, in order to solve the problem of variable aspect ratios of objects in urban street scenes, this article uses an anchor-free detector as the baseline detector. Since the anchor-free object detector can obtain neither explicit nor implicit instance-level features, we adopt Pixel-Level Adaptation (PLA) to align local features instead of instance-level alignment for local features. The size of the object has the greatest impact on the final detection effect, and the object scale in urban scenes is relatively rich. Guided by the differentiation of attention mechanisms, a multi-level adversarial network is designed to perform feature alignment of the output space at different feature levels called Scale Information Limitation (SIL). We conducted cross-domain detection experiments by using various urban streetscape autonomous driving object detection datasets, including adverse weather conditions, synthetic data to real data, and cross-camera adaptation. The experimental results indicate that the method proposed in this article is effective. © 2024 Elsevier B.V.