Every project has a beautiful feature showcase page. It’s easy to include images in a flexible 3-column grid format. Make your photos 1/3, 2/3, or full width.
To give your project a background in the portfolio page, just add the img tag to the front matter like so:
---
layout: page
title: project
description: a project with a background image
img: /assets/img/12.jpg
---
Caption photos easily. On the left, a road goes through a tunnel. Middle, leaves artistically fall in a hipster photoshoot. Right, in another hipster photoshoot, a lumberjack grasps a handful of pine needles.
This image can also have a caption. It's like magic.
You can also put regular text between your rows of images. Say you wanted to write a little bit about your project before you posted the rest of the images. You describe how you toiled, sweated, bled for your project, and then… you reveal its glory in the next row of images.
You can also have artistically styled 2/3 + 1/3 images, like these.
The code is simple. Just wrap your images with <div class="col-sm"> and place them inside <div class="row"> (read more about the Bootstrap Grid system). To make images responsive, add img-fluid class to each; for rounded corners and shadows use rounded and z-depth-1 classes. Here’s the code for the last row of images above:
<div class="row justify-content-sm-center">
<div class="col-sm-8 mt-3 mt-md-0">
{% include figure.html path="assets/img/6.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
</div>
<div class="col-sm-4 mt-3 mt-md-0">
{% include figure.html path="assets/img/11.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
</div>
</div>
Related Publications
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Optimizing Wind Turbine Surface Defect Detection: A Rotated Bounding Box Approach
Imad Gohar, Abderrahim Halimi, Yew Weng Kean, and John See
32nd European Conference on Signal Processing (to appear), 2024
Detecting surface defects on Wind Turbine Blades (WTBs) from remotely sensed images is a crucial step toward automated visual inspection. Typical object detection algorithms use standard bounding boxes to locate defects on WTBs. However, Oriented Bounding Boxes (OBBs) have been shown in cases of satellite imagery, to provide more precise localization of object regions and actual orientation. Existing WTB datasets do not depict defects using OBBs and this causes the lack of useful orientational information. In this paper, we consider OBBs for WTB surface defect detection through two publicly available datasets, introducing new annotations to the community. Baselines were constructed on state-of-the-art rotated object detectors, demonstrating considerable promise and known gaps that can be addressed in the future. We present a comprehensive analysis of their performances including ablation study and discussions on the importance of angular disparity between OBBs.
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Slice-Aided Defect Detection in Ultra High-Resolution Wind Turbine Blade Images
Imad Gohar, Abderrahim Halimi, John See, Weng Kean Yew, and Cong Yang
Machines, 2023
The processing of aerial images taken by drones is a challenging task due to their high resolution and the presence of small objects. The scale of the objects varies diversely depending on the position of the drone, which can result in loss of information or increased difficulty in detecting small objects. To address this issue, images are either randomly cropped or divided into small patches before training and inference. This paper proposes a defect detection framework that harnesses the advantages of slice-aided inference for small and medium-size damage on the surface of wind turbine blades. This framework enables the comparison of different slicing strategies, including a conventional patch division strategy and a more recent slice-aided hyper-inference, on several state-of-the-art deep neural network baselines for the detection of surface defects in wind turbine blade images. Our experiments provide extensive empirical results, highlighting the benefits of using the slice-aided strategy and the significant improvements made by these networks on an ultra high-resolution drone image dataset.
