A post-processing scheme for the performance improvement of vehicle detection in wide-area aerial imagery

Included is an Erratum to the article, published February, 2020.In this paper, we present a post-processing scheme to improve the performance of vehicle detection in wide-area aerial imagery. Using low-resolution aerial frames for the performance analysis, we adapted nine algorithms for vehicle detection. We derived a three-stage scheme to measure performance improvement on the selected five object segmentation algorithms before and after post-processing. We compared automatic detections results to ground-truth objects, and classified each type of detections in terms of true positive, false negative and false positive. Several evaluation metrics are adopted for the experimental study.12 month embargo; published online: 8 November 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu