Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An explainable deep vision system for animal classification and
  detection in trail-camera images with automatic post-deployment retraining   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.

28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio