Visual object detection model integrating deep active learning

The perception of surrounding objects by vehicle autonomous driving is an important means to ensure traffic safety. Object detection models based on deep learning are widely used, but they require a large amount of labeled data for training. This paper proposes an active visual object detection model that uses Gaussian mixture distribution to estimate the uncertainty of unlabeled images, reducing the dependence of model training on labeled data. First, a mixed density network is used as the detection head, and the image features extracted by the deep neural network are used as input to estimate the probability distribution of the classification and positioning of the target prediction box. Secondly, the classification score value of the target prediction box is mapped to the probability space, and the classification uncertainty of the target is calculated using the edge uncertainty; the positioning uncertainty of the target is measured using the prediction box positioning variance. Finally, the most unstable samples are selected for labeling. Compared with other typical active learning sampling strategies on the VOC dataset, the proposed method achieves the best performance, and the 54%data annotation amount can reach the performance of YOLOX supervised learning 98.8%, saving nearly 45% of the data annotation amount.