Abstract:

The perception of surrounding objects by vehicle autonomous driving is an important means to ensure traffic safety. Object detection model with deep learning is adopted widely， but they requires a large amount of annotated data for training. In this paper， an active vision object detection model is proposed using Gaussian mixture distribution to estimate the uncertainty of unlabeled images， reduces the dependence of model training on labeled data. Firstly， the mixed density network is adopted as the detection head， taking the image feature extracted by the deep neural network as input， estimates the probability distribution of classification and location of the object predicted boxes. Secondly， the classification score of the object predicted boxes is mapped into the probability space， and the classification uncertainty of the object is calculated by edge uncertainty； the location variance of the predicted boxes is used to measure the location uncertainty of object. Finally， the most unstable samples were selected for labeling. The results on the VOC dataset show that compared with other typical active learning sampling strategies， the proposed model achieved the best performance. The proposed model using only 54% of the data annotation volume can achieve the 98.8% performance of YOLOX with supervised learning， which saves up nearly 45% of the data annotation volume.

Key words: active learning, object detection, Gaussian distribution, labeling cost, uncertainty estimation