{'fraction_non_alphanumeric': 0.015819209039548022, 'fraction_numerical': 0.006779661016949152, 'mean_word_length': 5.283687943262412, '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}}
An Improved Image Segmentation Method of BiSeNetV2 Network

The task of image semantic segmentation is to annotate and segment the semantic information of different types of objects in the image, and predict the category and location information of objects. The difficulty lies in obtaining enough semantic information while retaining enough space information. In order to solve this problem, this paper proposes an improved BiSeNetV2 network. The main idea is to add DenseASPP module to detail branch to obtain larger receptive field, and add efficient channel attention (ECA) module to detail and semantic branch to optimize the feature graph extracted in each stage. so as to further improve the network acquisition. Experimental results show that the proposed algorithm improves the MIoU index by 1.62% on cityscapes dataset, and achieves better performance than BiSeNetV2 network.