Rethinking Drone-Based Search and Rescue with Aerial Person Detection

Abstract

The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today. Since this inspection is a slow, tedious and errorprone job for humans, we propose a novel deep learning algorithm to automate this aerial person detection (APD) task. We experiment with model architecture selection, online data augmentation, transfer learning, image tiling and several other techniques to improve the test performance of our method. We present the novel Aerial Inspection Reti-naNet (AIR) algorithm as the combination of these contributions. The AIR detector demonstrates state-of-the-art performance on a commonly used SAR test data set in terms of both precision (∼21 percentage point increase) and speed. In addition, we provide a new formal definition for the APD problem in SAR missions. That is, we propose a novel evaluation scheme that ranks detectors in terms of real-world SAR localization requirements. Finally, we propose a novel postprocessing method for robust, approximate object localization: the merging of overlapping bounding boxes (MOB) algorithm. This final processing stage used in the AIR detector significantly improves its performance and usability in the face of real-world aerial SAR missions.

HDetect-VS: Tiny Human Object Enhancement and Detection Based on Visual Saliency for Maritime Search and Rescue

“…When objects are concealed by sun glint, model performance is significantly poorer due to their weak features. In real-world SAR tasks, it is more important to find objects reliably, rather than accurately localizing them [48]. According to the evident clustering in maritime object distribution, we developed a two-step strategy to detect tiny objects in maritime SAR.…”

Semi-Supervised Person Detection in Aerial Images with Instance Segmentation and Maximum Mean Discrepancy Distance

“…When the two distributions are not identical, the moment with the greatest difference between them should be utilized as the measure of the two distributions. MMD [29] is a typical loss function used in migration learning and is frequently used to estimate the distance between two distributions, which can be simplified as Equation (6):…”

“…Researchers have attempted to create datasets of persons in aerial images and have obtained some promising results by utilizing existing creative object detection algorithms based on natural images. However, the results have been weak and lack robustness [5][6][7][8]. Compared to remote sensing objects [9][10][11][12][13][14][15], such as ships, vehicles, and airplanes, persons in aerial images are frequently costly to identify in SaR scenes, difficult to label manually, have fewer available datasets, and have multi-view shooting features that vary greatly.…”