{'fraction_non_alphanumeric': 0.009977827050997782, 'fraction_numerical': 0.0022172949002217295, 'mean_word_length': 6.110236220472441, '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}}
Combining Deep Learning and Robotics for Automated Concrete Delamination Assessment

Computer vision and robotics present tremendous opportunities for automating routine inspections of reinforced concrete bridges. One of the most critical aspects of these inspections is delamination assessment, as delaminations present immediate safety concerns due to falling concrete. Current methods of delamination assessment include hammer sounding and chain dragging, which are time consuming and difficult when accessibility is limited. Infrared technology presents an alternative method of assessing delaminations. In this work, a novel inspection method is proposed that uses an infrared camera combined with a convolutional neural network to automatically assess delaminations in infrared images. MobileNetV2 is implemented as an encoder with Deeplab V3 to perform pixel-wise segmentation in infrared images