Automatic Air-to-Ground Recognition of Outdoor Injured Human Targets Based on UAV Bimodal Information: The Explore Study

Abstract

The rapid air-to-ground search of injured people in the outdoor environment has been a hot spot and a great challenge for public safety and emergency rescue medicine. Its crucial difficulties lie in the fact that small-scale human targets possess a low target-background contrast to the complex outdoor environment background and the human attribute of the target is hard to verify. Therefore, an automatic recognition method based on UAV bimodal information is proposed in this paper. First, suspected targets were accurately detected and separated from the background based on multispectral feature information only. Immediately after, the bio-radar module would be released and would try to detect their corresponding physiological information for accurate re-identification of the human target property. Both the suspected human target detection experiments and human target property re-identification experiments show that our proposed method could effectively realize accurate identification of ground injured in outdoor environments, which is meaningful for the research of rapid search and rescue of injured people in the outdoor environment.

UAV multispectral multi-domain feature optimization for the air-to-ground recognition of outdoor injured human targets under cross-scene environment

“…To pick out these discriminative spectral bands, a large number of preliminary measurement experiments were carried out to obtain the wavelength-relative reflectivity curve for green vegetation (to simulate background) and green camouflage (to simulate suspected injured human target outdoors) using a spectrometer. Just as what we analyzed and validate in our previous paper ( 32 ), six specific bands, including the blue band (450 ± 3 nm), green band (555 ± 3 nm), red band (660 ± 3 nm), red edge (710 ± 3 nm) and near-infrared (840 ± 3 nm and 940 ± 3 nm), were selected as spectrum components for custom multispectral cameras. According to the requirements above, the MS600 camera shown in Figure 1B was adopted in our study.…”

“…Specifically, as shown in Figure 4 , a total of 50 features of these types were extracted to form the multi-domain feature description set F Global : Spectral reflectance feature F reflect : r b 1 to r b 6 are the radiation-corrected reflectance values corresponding to those six bands of multispectral images. F index are eight spectral index features and their calculation methods have been talked about in our previous paper ( 32 ), which could enhance detail characteristics by combining the reflectance values of multiple bands. Texture features F text : Texture is computed by the grayscale attribute of pixels and their neighbors, which helps to distinguish the phenomenon of “same-spectrum, different-spectrum”.…”

“…Spectral reflectance feature F reflect : r b 1 to r b 6 are the radiation-corrected reflectance values corresponding to those six bands of multispectral images. F index are eight spectral index features and their calculation methods have been talked about in our previous paper ( 32 ), which could enhance detail characteristics by combining the reflectance values of multiple bands.…”

“…Based on this advantage, PAR Government Systems Corporation even tried to detect and recognize the mine-like small target by adding a temporal dimension to the spectral processing ( 31 ) in desert background. In this regard, our group also tried to recognize the injured human targets in camouflage in a static outdoor environment based on UAV-multispectral features ( 32 ). In the latest research, researchers have even begun to study image target recognition under changing environmental factors (like illumination, seasonal and weather) based on deep learning ( 16 , 33 ), but the object and the environmental background objects always keep consistent.…”