{'fraction_non_alphanumeric': 0.02368421052631579, 'fraction_numerical': 0.0, 'mean_word_length': 4.8538461538461535, '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 Neural Network with Spatial Attention for Pixel-Level Crack Detection on Concrete Bridges

Concrete bridges play a very important role in transportation. As the main type of concrete bridges' damage, crack detection is of great significance to ensure the safety of bridges. In order to avoid the influence of subjective factors, methods based on deep learning develop rapidly. In this paper, a new network model in the form of encoder-decoder is proposed. It achieves the crack detection on pixel-level, which means that the detection results can be further quantified in the future. Meanwhile, the model proposed adds Spatial Attention to take advantage of crack's spatial characteristics. By doing this, more crack details can be found in the test results.