Generating object proposals for improved object detection in aerial images

摘要

Screening of aerial images covering large areas is important for many applications such as surveillance, tracing or rescue tasks. To reduce the workload of image analysts, an automatic detection of candidate objects is required. In general, object detection is performed by applying classifiers or a cascade of classifiers within a sliding window algorithm. However, the huge number of windows to classify, especially in case of multiple object scales, makes these approaches computationally expensive. To overcome this challenge, we reduce the number of candidate windows by generating so called object proposals. Object proposals are a set of candidate regions in an image that are likely to contain an object. We apply the Selective Search approach that has been broadly used as proposals method for detectors like R-CNN or Fast R-CNN. Therefore, a set of small regions is generated by initial segmentation followed by hierarchical grouping of the initial regions to generate proposals at different scales. To reduce the computational costs of the original approach, which consists of 80 combinations of segmentation settings and grouping strategies, we only apply the most appropriate combination. Therefore, we analyze the impact of varying segmentation settings, different merging strategies, and various colour spaces by calculating the recall with regard to the number of object proposals and the intersection over union between generated proposals and ground truth annotations. As aerial images differ considerably from datasets that are typically used for exploring object proposals methods, in particular in object size and the image fraction occupied by an object, we further adapt the Selective Search algorithm to aerial images by replacing the random order of generated proposals by a weighted order based on the object proposal size and integrate a termination criterion for the merging strategies. Finally, the adapted approach is compared to the original Selective Search algorithm and to baseline approaches like sliding window on the publicly available DLR 3K Munich Vehicle Aerial Image Dataset to show how the number of candidate windows to classify can be clearly reduced.