{'fraction_non_alphanumeric': 0.0255, 'fraction_numerical': 0.0055, 'mean_word_length': 5.67, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Fast and Efficient Vehicle Image Semantic Segmentation Data Augmentation Algorithm Based on LQE Head and BAS-DP

Aiming at the problem that it takes too long to manually label numerous semantic segmentation data sets of vehicle images, a fast and effective vehicle image semantic segmentation data augmentation algorithm is proposed. Firstly, to solve the problem that traditional data augmentation algorithms are difficult to generate vehicle images and corresponding labels at the same time, a vehicle image semantic segmentation data augmentation algorithm based on FCN (Fully Convolutional Network) and GCIoU (Generally Contour Intersection over Union) is proposed, which can simultaneously generate vehicle images and corresponding labels. Then, aiming at the problem that some low-quality data exist in the generated dataset, a data set quality discriminator based on LQE (Label Quality Evaluation) head is proposed. The discriminator can distinguish between low-quality and high-quality label files. Finally, aiming at the problem that the excessive weight of the label file causes the calculation speed to decrease, a lightweight algorithm for the label file based on BAS-DP (Beetle Antennae Search Douglas-Peucker Algorithm) is proposed. The lightweight algorithm can greatly decrease parameters of the label file and improve availability of data-augmented results. Experimental results show that the proposed data augmentation algorithm is better than DCGAN (Deep Convolutional Generative Adversarial Networks), WGAN (Wasserstein Generative Adversarial Networks) and other data augmentation algorithms in accuracy. The AP50 and AP75 of the proposed algorithm reach 0.924 and 0.41, respectively. In addition, the proposed data augmentation algorithm still performs well in scenes with single-object, multi-object and ultra-multi-object. Simultaneously, the proposed data augmentation algorithm has three advantages, which are higher accuracy, faster speed, and less training data required.