Special Issue "Deep Learning Based Object Detection"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editor

Dr. Youngbae Hwang
E-Mail Website
Guest Editor
Department of Electronics Engineering, College of Electrical and Computer Engineering (ECE), Chungbuk National University (CBNU), Korea
Interests: low-level image processing; deep learning based object detection/classification multi-task learning; network compression; medical image processing

Special Issue Information

Dear Colleagues,

Object detection is one of the most important and challenging categories of computer vision and machine learning, which have been extensively utilized in various applications, such as video surveillance, autonomous vehicle, human–machine interaction, medical image analysis, and so on. Recently, significant improvement has been achieved as a result of the rapid development of deep learning, especially convolutional neural networks (CNNs).

To evaluate deep learning-based object detection methods, various databases have been introduced, and many researchers have endeavored to improve the performance of their proposed methodologies for the target database. There are mainstream benchmarks based on general object detection datasets, such as ImageNet, KITTI, and MS COCO. Even though significant improvements were achieved from previous shallow network-based methods for well-known datasets, unseen data from different environments or different applications suffered from relatively low performance.

This Special Issue will cover the most recent technical advances in all deep learning-based object recognition aspects, including theoretical issues on deep learning, real-world applications, practical object detection systems, and originally designed databases. Both transfer learning or semi-supervised learning of deep learning are welcome. Reviews and surveys of the state-of-the-art in deep learning-based object detection are also welcome. Topics of interest for this Special Issue include, but are not limited to, the following topics:

  • Image/video-based object detection using deep learning
  • Sensor fusion for object detection using deep learning
  • Transfer learning for object detection
  • Online learning for object detection
  • Active learning for object detection
  • Semi-supervised learning for object detection
  • Deep learning-based object detection for real-world applications
  • Object detection systems
  • New database for object detection
  • Survey for deep learning-based object detection

Dr. Youngbae Hwang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Open AccessArticle
Object Detection Algorithm Based on Improved YOLOv3
Electronics 2020, 9(3), 537; https://doi.org/10.3390/electronics9030537 - 24 Mar 2020
The ‘You Only Look Once’ v3 (YOLOv3) method is among the most widely used deep learning-based object detection methods. It uses the k-means cluster method to estimate the initial width and height of the predicted bounding boxes. With this method, the estimated width [...] Read more.
The ‘You Only Look Once’ v3 (YOLOv3) method is among the most widely used deep learning-based object detection methods. It uses the k-means cluster method to estimate the initial width and height of the predicted bounding boxes. With this method, the estimated width and height are sensitive to the initial cluster centers, and the processing of large-scale datasets is time-consuming. In order to address these problems, a new cluster method for estimating the initial width and height of the predicted bounding boxes has been developed. Firstly, it randomly selects a couple of width and height values as one initial cluster center separate from the width and height of the ground truth boxes. Secondly, it constructs Markov chains based on the selected initial cluster and uses the final points of every Markov chain as the other initial centers. In the construction of Markov chains, the intersection-over-union method is used to compute the distance between the selected initial clusters and each candidate point, instead of the square root method. Finally, this method can be used to continually update the cluster center with each new set of width and height values, which are only a part of the data selected from the datasets. Our simulation results show that the new method has faster convergence speed for initializing the width and height of the predicted bounding boxes and that it can select more representative initial widths and heights of the predicted bounding boxes. Our proposed method achieves better performance than the YOLOv3 method in terms of recall, mean average precision, and F1-score. Full article
(This article belongs to the Special Issue Deep Learning Based Object Detection)
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