Abstract
SNOW-COVERED SIDEWALKS POSE SIGNIFICANT SAFETY HAZARDS, ESPECIALLY FOR VULNERABLE POPULATIONS SUCH AS THE ELDERLY AND VISUALLY IMPAIRED. THE DEVELOPMENT OF EFFECTIVE SNOW DETECTION SYSTEMS IS CRUCIAL FOR ENHANCING PEDESTRIAN SAFETY. THIS RESEARCH AIMS TO ADDRESS THESE CHALLENGES BY DEVELOPING A SNOW DETECTION ALGORITHM SPECIFICALLY DESIGNED FOR SIDEWALKS. THE PROPOSED ALGORITHM USES A CONVOLUTIONAL NEURAL NETWORK (CNN) ARCHITECTURE INCORPORATING A 2-DIMENSIONAL SPATIAL ATTENTION MECHANISM TO FOCUS ON RELEVANT FEATURES IN IMAGES, IMPROVING SNOW DETECTION ACCURACY. DUE TO THE SEASONAL AND GEOGRAPHIC LIMITATIONS OF SNOW DATA COLLECTION, SYNTHETIC DATA GENERATION USING INVERSE DIFFUSION MODELS WAS EMPLOYED TO AUGMENT THE REAL-WORLD DATASET. ALTHOUGH THE INVERSE DIFFUSION-GENERATED IMAGES DID NOT SIGNIFICANTLY IMPROVE DETECTION PERFORMANCE COMPARED TO REAL-WORLD TEST DATA, THEY PROVIDED VALUABLE INSIGHTS INTO THE POTENTIAL FOR SYNTHETIC DATA AUGMENTATION. THE MODEL'S PERFORMANCE, EVALUATED THROUGH STANDARD METRICS LIKE RECALL, PRECISION, AND F2 SCORE, CONSISTENTLY SURPASSED ESTABLISHED MODELS SUCH AS FINE-TUNED VGG-19 AND RESNET-50. MOREOVER, THE MODEL DEMONSTRATED FASTER INFERENCE SPEEDS, MAKING IT SUITABLE FOR REAL-TIME APPLICATIONS. THE FINDINGS OF THIS STUDY UNDERSCORE THE IMPORTANCE OF DEVELOPING SPECIALIZED MODELS FOR SNOW DETECTION AND HIGHLIGHT THE ONGOING NEED FOR REFINING SYNTHETIC DATA GENERATION TECHNIQUES TO IMPROVE REAL-WORLD APPLICABILITY.
Advisor
Rajeev Bukralia
Committee Member
Mansi Bhavsar
Date of Degree
2024
Language
english
Document Type
Thesis
Degree
Master of Science (MS)
Program of Study
Data Science
Department
Mathematics and Statistics
College
Science, Engineering and Technology
Recommended Citation
deDeijn, Ricardo. (2024). Developing a Snow Detection Algorithm Using Spatial Attention for Pedestrian Safety [Master’s thesis, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/1475/
Creative Commons License
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