# An Object Detection Method Based on Feature Uncertainty Domain Adaptation for Autonomous Driving
## 1 Introduction  
Automatic driving vehicles require the accurate detection of surrounding objects to enable appropriate planning from subsequent decision-making algorithms. In recent years, deep-learning-based object detection algorithms [1; 2; 3; 4; 5; 6; 7; 8; 9] have achieved remarkable results, and many publicly available datasets have been used to evaluate algorithm performance. These models often rely on supervised training on large-scale datasets with ground truth annotations. However, compared to actual driving environments, the scenes contained in the datasets are mostly under good weather conditions. Training a detector based on such data (source domain) would result in the model learning only limited feature representations. When the scene changes, the pre-trained detector would suffer from a sharp decline in performance on data with different distributions (target domain). Therefore, improving the scene adaptation ability of the detector is an indispensable research topic in future automatic driving technology. Real-world traffic scenes present various difficulties in achieving accurate object detection due to different weather conditions, background styles, and target types. For example, rainy and foggy conditions can cause object occlusion, increased texture granularity, and blurred contours, making it difficult for the feature extraction layer of convolutional neural networks to obtain effective feature information, resulting in output bounding box offset and classification errors [10; 11; 12]. There are two different methods to solve this problem. The direct method involves continuously collecting data from various scenarios and obtaining corresponding labelsthrough manual annotation. This approach is an attempt to combat infinite possibilities with limited resources and is not a sustainable route. Another method is to adopt domain adaptation to obtain domain-invariant features from unlabeled data or guide the model to learn features of the target domain [13]. The purpose of domain adaptation is to use both labeled source domain data and unlabeled target domain data to train the model. This process is usually subject to two mutually adversarial constraints, guiding the model to reduce the difference in feature distribution between domains. In most domain adaptation-based object detection methods, additional domain discriminators are added after the feature extraction stage and detection head to solve the domain drift problem through adversarial learning. However, directly applying domain discriminators on the feature maps obtained from the feature extraction layer for local alignment can cause changes to the already aligned features, which, in turn, affects the detection accuracy. For autonomous driving scenarios, changes in target features due to environmental variations are often local. As shown in Figure 1a, both foggy and clear images contain the same object. Although the targets in the foggy images appear blurry, most of their features are similar to those in the normal images. This means that the model should focus on aligning regions with significant feature changes. Therefore, we propose a feature uncertainty-based local alignment module, Feature Uncertainty Alignment (FUA), that can adaptively achieve feature alignment based on the local blurriness of an image. Figure 1b displays two feature channel entropy maps from different domains, where each pixel value represents the entropy of all channels at that location. A higher channel entropy indicates that the position contains richer feature information, which is advantageous for target detection. Conversely, when feature information is insufficient, the model needs to learn new feature representations to adapt to the target domain. The methods based on adversarial learning have been widely developed in the fields of classification [14; 15; 16; 17; 18; 19] and segmentation [20; 21; 22; 23].