Training Object Detection And Recognition CNN Models Using Data Augmentation

Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding challenge in the field of computer vision. Specifically, object detection in drone images is a complex task due to objects of various scales such as humans, buildings, water bodies, and hills. In this paper, we present an implementation of ensemble transfer learning to enhance the performance of the base models for multiscale object detection in drone imagery. Combined with a test-time augmentation pipeline, the algorithm combines different models and applies voting strategies to detect objects of various scales in UAV images. The data augmentation also presents a solution to the deficiency of drone image datasets. We experimented with two specific datasets in the open domain: the VisDrone dataset and the AU-AIR Dataset. Our approach is more practical and efficient due to the use of transfer learning and two-level voting strategy ensemble instead of training custom models on entire datasets. The experimentation shows significant improvement in the mAP for both VisDrone and AU-AIR datasets by employing the ensemble transfer learning method. Furthermore, the utilization of voting strategies further increases the 3reliability of the ensemble as the end-user can select and trace the effects of the mechanism for bounding box predictions.

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Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

Applied Sciences ◽

10.3390/app11156721 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6721

Author(s):

Jinyeong Wang ◽

Sanghwan Lee

Keyword(s):

Neural Networks ◽

Machine Vision ◽

Object Detection ◽

Image Classification ◽

Convolutional Neural Networks ◽

Data Augmentation ◽

Image Data ◽

Manufacturing Productivity ◽

Smart Factories ◽

Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

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UAV Image Multi-Labeling with Data-Efficient Transformers

Applied Sciences ◽

10.3390/app11093974 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3974

Author(s):

Laila Bashmal ◽

Yakoub Bazi ◽

Mohamad Mahmoud Al Rahhal ◽

Haikel Alhichri ◽

Naif Al Ajlan

Keyword(s):

Data Augmentation ◽

Feature Representation ◽

Aerial Image ◽

Remote Sensing Images ◽

Training Set ◽

Proposed Model ◽

Class Labels ◽

Using Data ◽

Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

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