Deep Learning-Based Visual Identification of Signs of Bat Presence in Bridge Infrastructure Images: A Transfer Learning Approach

Abstract

Bat inventory surveys on bridges, structures, and dwellings are an important step in protecting threatened and endangered bat species that use the infrastructure as roosting locations. Observing guano droppings and staining is a common indicator of bat presence, but it can be difficult to verify whether certain stains originated from bats or other sources such as water seeps, rust staining, asphalt leaching, or other structural deterioration mechanisms. While humans find it hard to distinguish bat indicators without training, from a computer vision perspective they show different features that, coupled with expert opinion, can be used for automated detection of bat presence. To facilitate bat presence detection and streamline bat surveys, this paper leverages recent advances in visual recognition using deep learning to develop an image classification system that identifies bat indicators. An array of state-of-the-art convolutional neural networks were investigated. To overcome the shortage of data, parameters previously trained on large-scale datasets were used to transfer the learned feature representations. Using a pool of digital photographs collected by Virginia Department of Transportation (VDOT), a visual recognition model was developed and achieved 92.0% accuracy during testing. To facilitate the application of the developed model, a prototype web application was created to allow users to interactively upload images of stains on structures and receive classification results from the model. The web application is being deployed by VDOT in a pilot study and the success of the proposed approach is expected to help facilitate bat inventory surveys and the resulting conservation efforts.