Deep Learning Approach in Aerial Imagery for Supporting Land Search and Rescue Missions

Božić-Štulic, Dunja; Marušić, Željko; Gotovac, S.

doi:10.1007/s11263-019-01177-1

Cited by 69 publications

(70 citation statements)

References 18 publications

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“…In [24], the authors tried different approaches, applying and analyzing various salient detection algorithms to detect lost persons. In [4], the authors proposed a two-stage algorithm approach based on salient detection and convolution neural networks. This approach showed promising results, but the false alarm rate was reported as an issue.…”

Section: Related Workmentioning

confidence: 99%

Visual-Based Person Detection for Search-and-Rescue with UAS: Humans vs. Machine Learning Algorithm

et al. 2020

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Unmanned Aircraft Systems (UASs) have been recognized as an important resource in search-and-rescue (SAR) missions and, as such, have been used by the Croatian Mountain Search and Rescue (CMRS) service for over seven years. The UAS scans and photographs the terrain. The high-resolution images are afterwards analyzed by SAR members to detect missing persons or to find some usable trace. It is a drawn out, tiresome process prone to human error. To facilitate and speed up mission image processing and increase detection accuracy, we have developed several image-processing algorithms. The latest are convolutional neural network (CNN)-based. CNNs were trained on a specially developed image database, named HERIDAL. Although these algorithms achieve excellent recall, the efficiency of the algorithm in actual SAR missions and its comparison with expert detection must be investigated. A series of mission simulations are planned and recorded for this purpose. They are processed and labelled by a developed algorithm. A web application was developed by which experts analyzed raw and processed mission images. The algorithm achieved better recall compared to an expert, but the experts achieved better accuracy when they analyzed images that were already processed and labelled.

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Section: Related Workmentioning

confidence: 99%

Visual-Based Person Detection for Search-and-Rescue with UAS: Humans vs. Machine Learning Algorithm

et al. 2020

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“…Marušić et al in [25] performed person detection on UAV images from the HERIDAL dataset in the visible spectrum using Faster R-CNN as a backbone. Furthermore, Božić-Štulić et al [26] dealt with the same problem. They used a visual attention algorithm for the detection of salient objects in images in order to reduce the large-scale search space.…”

Section: Search and Rescue Operationsmentioning

confidence: 99%

“…In the end, non maxima suppression was performed for reducing false positive detections by clustering proposals by spatial closeness, and the achieved detection rate was 88.9%, while the precision was 34.8%, which make their methods currently the state-of-the-art for this particular problem. Since our research was performed on the same dataset, it is most appropriate to compare our results with those mentioned above and presented in [25,26].…”

Section: Search and Rescue Operationsmentioning

confidence: 99%

“…However, the problem of detecting persons in aerial imagery for search and rescue purposes is very specific, so it is not possible to use standard image datasets. In order to solve this problem, research from the University of Split [26] published a dataset named HERIDAL, which is also used in this paper. The dataset contains 1677 images of wilderness at various locations acquired from an aerial perspective with an Unmanned Aerial Vehicle (UAV), also known as a drone, with a high definition camera.…”

Section: Dataset Descriptionmentioning

confidence: 99%

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Multimodel Deep Learning for Person Detection in Aerial Images

Vasić

Papić

2020

Electronics

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In this paper, we propose a novel method for person detection in aerial images of nonurban terrain gathered by an Unmanned Aerial Vehicle (UAV), which plays an important role in Search And Rescue (SAR) missions. The UAV in SAR operations contributes significantly due to the ability to survey a larger geographical area from an aerial viewpoint. Because of the high altitude of recording, the object of interest (person) covers a small part of an image (around 0.1%), which makes this task quite challenging. To address this problem, a multimodel deep learning approach is proposed. The solution consists of two different convolutional neural networks in region proposal, as well as in the classification stage. Additionally, contextual information is used in the classification stage in order to improve the detection results. Experimental results tested on the HERIDAL dataset achieved precision of 68.89% and a recall of 94.65%, which is better than current state-of-the-art methods used for person detection in similar scenarios. Consequently, it may be concluded that this approach is suitable for usage as an auxiliary method in real SAR operations.

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“…While it is now rather easy to find eye tracking data on typical images [35,[37][38][39][40][41][42][43][44][45] or videos [46][47][48][49][50], and that there are many UAV content datasets [7,[51][52][53][54][55][56][57][58][59][60][61][62], it turns out to be extremely difficult to find eye-tracking data on UAV content. This is even truer when we consider dynamic salience, which refers to salience for video content.…”

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confidence: 99%

EyeTrackUAV2: A Large-Scale Binocular Eye-Tracking Dataset for UAV Videos

Perrin

Krassanakis

Zhang

et al. 2020

Drones

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The fast and tremendous evolution of the unmanned aerial vehicle (UAV) imagery gives place to the multiplication of applications in various fields such as military and civilian surveillance, delivery services, and wildlife monitoring. Combining UAV imagery with study of dynamic salience further extends the number of future applications. Indeed, considerations of visual attention open the door to new avenues in a number of scientific fields such as compression, retargeting, and decision-making tools. To conduct saliency studies, we identified the need for new large-scale eye-tracking datasets for visual salience in UAV content. Therefore, we address this need by introducing the dataset EyeTrackUAV2. It consists of the collection of precise binocular gaze information (1000 Hz) over 43 videos (RGB, 30 fps, 1280 × 720 or 720 × 480). Thirty participants observed stimuli under both free viewing and task conditions. Fixations and saccades were then computed with the dispersion-threshold identification (I-DT) algorithm, while gaze density maps were calculated by filtering eye positions with a Gaussian kernel. An analysis of collected gaze positions provides recommendations for visual salience ground-truth generation. It also sheds light upon variations of saliency biases in UAV videos when opposed to conventional content, especially regarding the center bias.

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Deep Learning Approach in Aerial Imagery for Supporting Land Search and Rescue Missions

Cited by 69 publications

References 18 publications

Visual-Based Person Detection for Search-and-Rescue with UAS: Humans vs. Machine Learning Algorithm

Visual-Based Person Detection for Search-and-Rescue with UAS: Humans vs. Machine Learning Algorithm

Multimodel Deep Learning for Person Detection in Aerial Images

EyeTrackUAV2: A Large-Scale Binocular Eye-Tracking Dataset for UAV Videos

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