{'fraction_non_alphanumeric': 0.01499605367008682, 'fraction_numerical': 0.003946329913180742, 'mean_word_length': 5.308457711442786, '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}}
Smart Elevator Obstruction Detection System using Image Classification

—This paper proposes an approach that leverages real-time Image Classification to improve elevator safety. Elevators are a necessity for most multi story buildings. As a result, they play a crucial role in the lives of millions of people around the world. Despite this, there has been limited advancement in the technology used for elevator door operators. In the current system, elevators use multiple infrared transmitter/receiver pair of sensors to detect obstructions between the doors. This does not effectively detect smaller objects such as pets, small children, pet leashes etc. between the elevator doors which has led to thousands of tragic fatalities. This paper proposes an approach to tackle this challenge by leveraging Binary Image Classification to determine whether there is an obstruction between the elevator doors. This study includes the construction of a novel dataset of over 10,000 images and a comprehensive evaluation and comparison of several Machine Learning models for the proposed system. The results have produced novel findings that can be used to significantly improve safety and reliability of elevator door operators by preventing tragic fatalities every year.