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. 2022 Jan 15:2022:1549842.
doi: 10.1155/2022/1549842. eCollection 2022.

A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery

Amr Abozeid  1 , Rayan Alanazi  1 , Ahmed Elhadad  1 , Ahmed I Taloba  1 , Rasha M Abd El-Aziz  1
Affiliations

A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery

Amr Abozeid et al. Comput Intell Neurosci. .

Abstract

Since the Pre-Roman era, olive trees have a significant economic and cultural value. In 2019, the Al-Jouf region, in the north of the Kingdom of Saudi Arabia, gained a global presence by entering the Guinness World Records, with the largest number of olive trees in the world. Olive tree detecting and counting from a given satellite image are a significant and difficult computer vision problem. Because olive farms are spread out over a large area, manually counting the trees is impossible. Moreover, accurate automatic detection and counting of olive trees in satellite images have many challenges such as scale variations, weather changes, perspective distortions, and orientation changes. Another problem is the lack of a standard database of olive trees available for deep learning applications. To address these problems, we first build a large-scale olive dataset dedicated to deep learning research and applications. The dataset consists of 230 RGB images collected over the territory of Al-Jouf, KSA. We then propose an efficient deep learning model (SwinTUnet) for detecting and counting olive trees from satellite imagery. The proposed SwinTUnet is a Unet-like network which consists of an encoder, a decoder, and skip connections. Swin Transformer block is the fundamental unit of SwinTUnet to learn local and global semantic information. The results of an experimental study on the proposed dataset show that the SwinTUnet model outperforms the related studies in terms of overall detection with a 0.94% estimation error.

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Conflict of interest statement

The authors declare that they have no conflicts of interest to report regarding the present study.

Figures

Figure 1
Figure 1
The architecture of the SwinTUnet model includes encoders, decoders, and skip connections. The Swin Transformer is the base of building the model.
Figure 2
Figure 2
Two sequential Swin Transformer blocks.
Figure 3
Figure 3
Sample olive images (the first column shows original images, the second column shows ground truth, while the third column shows corresponding detection results).
Figure 4
Figure 4
Performance comparison of the proposed model and the existing work.
See this image and copyright information in PMC

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