Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles

Kamilaris, Andreas; Brink, Corjan van den; Karatsiolis, Savvas

doi:10.1007/978-3-030-29930-9_8

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…Discriminative models are used for predictions/ classifications, whereas generative models are used for synthesis/generation of data similar to the input data set. The use of generative data to train DL models is promising, with early attempts in agriculture indicating positive outcomes [16]. Discriminative models, focused on predictions of the precise number of targets in an image, i.e., counting, are employed herein.…”

Section: Related Workmentioning

confidence: 99%

“…Compared to traditional techniques, such as Support Vector Machines and Random Forests, DL has demonstrated enhanced performance in classification and counting computer vision-related problems [15]. A preliminary effort in this direction can be found in [16], where real and synthetic data were used together to count the number of houses from aerial photographs acquired by unmanned aerial vehicles (UAVs).…”

Section: Introductionmentioning

confidence: 99%

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Counting sea lions and elephants from aerial photography using deep learning with density maps

et al. 2021

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Background The ability to automatically count animals is important to design appropriate environmental policies and to monitor their populations in relation to biodiversity and maintain balance among species. Out of all living mammals on Earth, 60% are livestock, 36% humans, and only 4% are animals that live in the wild. In a relatively short period, development of human civilization caused a loss of 83% of wildlife and 50% of plants. The rate of species extinction is accelerating. Traditional wildlife surveys provide rough population estimates. However, emerging technologies, such as aerial photography, allow to perform large-scale surveys in a short period of time with high accuracy. In this paper, we propose the use of computer vision, through deep learning (DL) architecture, together with aerial photography and density maps, to count the population of Steller sea lions and African elephants with high precision. Results We have trained two deep learning models, a basic UNet without any feature extractor (Model-1) and another with the EfficientNet-B5 feature extractor (Model-2). We measured the model’s prediction accuracy, using Root Mean Square Error (RMSE) for the predicted and actual animal count. The results showed an RMSE of 1.88 and 0.60 to count Steller sea lions and African elephants, respectively, regardless of complex background, different illumination conditions, heavy overlapping and occlusion of the animals. Conclusions Our proposed solution performed very well in the counting prediction problem, with relatively low training parameters and minimum annotation. The approach adopted, combining DL and density maps, provided better results than state-of-art deep learning models used for counting, indicating that the proposed method has the potential to be used more widely in large-scale wildlife surveying projects and initiatives.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Counting sea lions and elephants from aerial photography using deep learning with density maps

et al. 2021

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“…However, in recent years, DL-based algorithms and architectures have proven to be superior in difficult classification tasks, as illustrated by Trung et al [55], Wang et al [56], and Bui et al [57]. DL methods trained on synthetic data have achieved satisfactory results on real data as well [58]. Barth et al [59] reported better classification results in the modelling of artificial conditions (training/testing on synthetic images or controlled lighting conditions) to improve the segmentation of the details of yellow pepper.…”

Section: And Cnn In Generic Object Detection In Agriculturementioning

confidence: 99%

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

2021

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Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.

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“…The aforementioned datasets are presented in Table 3. Finally, in another paper [61], the dataset was a combination from web-searched images, with a proposed method of synthesized image datasets [62].…”

Section: Datasetsmentioning

confidence: 99%

Computer Vision for Fire Detection on UAVs—From Software to Hardware

2021

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Fire hazard is a condition that has potentially catastrophic consequences. Artificial intelligence, through Computer Vision, in combination with UAVs has assisted dramatically to identify this risk and avoid it in a timely manner. This work is a literature review on UAVs using Computer Vision in order to detect fire. The research was conducted for the last decade in order to record the types of UAVs, the hardware and software used and the proposed datasets. The scientific research was executed through the Scopus database. The research showed that multi-copters were the most common type of vehicle and that the combination of RGB with a thermal camera was part of most applications. In addition, the trend in the use of Convolutional Neural Networks (CNNs) is increasing. In the last decade, many applications and a wide variety of hardware and methods have been implemented and studied. Many efforts have been made to effectively avoid the risk of fire. The fact that state-of-the-art methodologies continue to be researched, leads to the conclusion that the need for a more effective solution continues to arouse interest.

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Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles

Cited by 11 publications

References 22 publications

Counting sea lions and elephants from aerial photography using deep learning with density maps

Counting sea lions and elephants from aerial photography using deep learning with density maps

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

Computer Vision for Fire Detection on UAVs—From Software to Hardware

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