{'fraction_non_alphanumeric': 0.02037914691943128, 'fraction_numerical': 0.006161137440758294, 'mean_word_length': 4.720867208672087, '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}}
University of Oslo

Road tunnels are often filled with heavily polluted air, which is dangerous to the driver and passengers. The purpose of a Road Tunnel Entrance Recognition System (RTERS) is to automatically protect the driver and passengers from dangers involved when entering a tunnel. These actions include switching to recirculation of air to avoid “inhaling” pollution from the road tunnel, ensuring that the instrument lights are on and adjusting the headlights. In addition the system can alert the driver of any open windows and remove the dew that might build up on the front window when entering a cold tunnel. This thesis describes work undertaken on developing and testing image recognition methods for detecting road tunnel entrances to be a part of a future RTERS. The system takes a color image as input, evaluate the image and decides if the image contains a road tunnel entrance or not. These methods utilize different features in road tunnel entrances and the best methods are combined to create a more effective hybrid method. The methods in the RTERS are: • Tunnel Arch Detection (Circle Hough Transform is used to detect the tunnel arch). • Consecutive Tunnel Lights (Searches for lanes of tunnel light objects). • Elliptic Black Hole (Marks the tunnel entrance with the help of Lane Tracking and evaluates the dark tunnel entrance shape). • Template Matching (Checks the image for matches to a template of a general tunnel entrance). From these methods, two hybrids were developed: Hybrid 1: A combination of Tunnel Arch Detection and Consecutive Tunnel Lights Hybrid 2: A combination of Elliptic Black Hole and Consecutive Tunnel Lights The final choice of method for the RTERS was Hybrid 2. This program gives an average of 82% correct classification on the two test sets used in the thesis. Hybrid 2 also has an average run-time of 1.4 seconds, which is short enough for the method to evaluate two images in the Photo Capturing Zone. This is the zone that the methods are designed to work within and encompasses the distance between 120 meters to 40 meters from the tunnel entrance.