{'fraction_non_alphanumeric': 0.021153846153846155, 'fraction_numerical': 0.0, 'mean_word_length': 4.88135593220339, '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}}
Phase detection of an alternating laser heat source in infrared images using Bayesian recursive filtering

The detection of cracks on metallic parts is critical in many industrial applications. Thermography is one of the most effective methods for this purpose. In laser heating the infrared images are strongly affected by laser reflection or secondary emission. To avoid this problem, during the measurement, the laser beam can be periodically alternated between on and off states. For this technique to be effective, it is important to take into account the phase of the laser (e.g. on/off state) at each image frame. Due to the noisy and non-stationary nature of the observed real and apparent temperature of the surface at the location of the laser spot, it is challenging to detect the phase of laser pulse. In this work, Bayesian recursive filtering (BRF) is successfully applied for achieving this goal. This method can seamlessly account for non-periodic conditions and inaccuracies in laser modulation frequency and sampling rate.