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Leveraging gradient weighted class activation mapping to improve classification effectiveness: Case study in transportation i...

Publication Type
Conference Paper
Journal Name
Electronic Imaging
Publication Date
Conference Name
Electronic Imaging 2022
Conference Location
Burlingame, California, United States of America
Conference Sponsor
Society for Imaging Science and Technology
Conference Date

Roadway “corners” are common for pedestrian use, whether designated with markings or not. Different types of markings have been deployed, ranging from simple parallel lines to more complex designs. Understanding the impact of different types of crosswalks is important for public safety. In this work we explore methods to improve the logging of marked crosswalk types. We used the Roadway Information Database from the Second Strategic Highway Research Project and used active learning methods with transfer learning to identify the crosswalk types (marked or unmarked). Upon completion we found our classifiers were unable to perform above roughly 94% correct classifications. To improve their efficacy, we separated the crosswalks into their “fine grained” types and used Gradient-Weighted Class Activation Mapping to isolate and study the features that classified the crosswalks. We compared this with sampled manually marked crosswalks and present findings. We believe this use case can represent a process to improve the active learning method for some visual machine learning applications.