An explainable deep vision system for animal classification and detection in trail-camera images with automatic post-deployment retraining

TL;DR

This paper presents an automated, explainable deep vision system for animal detection in trail-camera images, featuring high accuracy, rapid processing, and automatic retraining to adapt to seasonal data variations.

Contribution

The paper introduces a two-stage deep learning pipeline with an innovative automatic retraining method to handle data drift in wildlife image classification.

Findings

Animal classification sensitivity 93%, specificity 96%

Bird detection sensitivity 93%, specificity 92%

System processes images in less than 0.5 seconds

Abstract

This paper introduces an automated vision system for animal detection in trail-camera images taken from a field under the administration of the Texas Parks and Wildlife Department. As traditional wildlife counting techniques are intrusive and labor intensive to conduct, trail-camera imaging is a comparatively non-intrusive method for capturing wildlife activity. However, given the large volume of images produced from trail-cameras, manual analysis of the images remains time-consuming and inefficient. We implemented a two-stage deep convolutional neural network pipeline to find animal-containing images in the first stage and then process these images to detect birds in the second stage. The animal classification system classifies animal images with overall 93% sensitivity and 96% specificity. The bird detection system achieves better than 93% sensitivity, 92% specificity, and 68% average Intersection-over-Union rate. The entire pipeline processes an image in less than 0.5 seconds as opposed to an average 30 seconds for a human labeler. We also addressed post-deployment issues related to data drift for the animal classification system as image features vary with seasonal changes. This system utilizes an automatic retraining algorithm to detect data drift and update the system. We introduce a novel technique for detecting drifted images and triggering the retraining procedure. Two statistical experiments are also presented to explain the prediction behavior of the animal classification system. These experiments investigate the cues that steers the system towards a particular decision. Statistical hypothesis testing demonstrates that the presence of an animal in the input image significantly contributes to the system's decisions.