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Electronics
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Deep Learning Based Object Detection II
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Deep Learning Based Object Detection II

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 13748

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), Cheongju-si, Korea
Interests: low-level image processing; deep learning based object detection/classification multi-task learning; network compression; medical image processing
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 semimonthly 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 2400 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 (6 papers)

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Research

14 pages, 8575 KiB  
Article
Mask Detection Method Based on YOLO-GBC Network
by Changqing Wang, Bei Zhang, Yuan Cao, Maoxuan Sun, Kunyu He, Zhonghao Cao and Meng Wang
Electronics 2023, 12(2), 408; https://doi.org/10.3390/electronics12020408 - 13 Jan 2023
Cited by 6 | Viewed by 1841
Abstract
For the problems of inaccurate recognition and the high missed detection rate of existing mask detection algorithms in actual scenes, a novel mask detection algorithm based on the YOLO-GBC network is proposed. Specifically, in the backbone network part, the global attention mechanism (GAM) [...] Read more.
For the problems of inaccurate recognition and the high missed detection rate of existing mask detection algorithms in actual scenes, a novel mask detection algorithm based on the YOLO-GBC network is proposed. Specifically, in the backbone network part, the global attention mechanism (GAM) is integrated to improve the ability to extract key information through cross-latitude information interaction. The cross-layer cascade method is adopted to improve the feature pyramid structure to achieve effective bidirectional cross-scale connection and weighted feature fusion. The sampling method of content-aware reassembly of features (CARAFE) is integrated into the feature pyramid network to fully retain the semantic information and global features of the feature map. NMS is replaced with Soft-NMS to improve model prediction frame accuracy by confidence decay method. The experimental results show that the average accuracy (mAP) of the YOLO-GBC reached 91.2% in the mask detection data set, which is 2.3% higher than the baseline YOLOv5, and the detection speed reached 64FPS. The accuracy and recall have also been improved to varying degrees, increasing the detection task of correctly wearing masks. Full article
(This article belongs to the Special Issue Deep Learning Based Object Detection II)
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13 pages, 3656 KiB  
Article
Automatic Pavement Crack Detection Fusing Attention Mechanism
by Junhua Ren, Guowu Zhao, Yadong Ma, De Zhao, Tao Liu and Jun Yan
Electronics 2022, 11(21), 3622; https://doi.org/10.3390/electronics11213622 - 6 Nov 2022
Cited by 11 | Viewed by 1892
Abstract
Pavement cracks can result in the degradation of pavement performance. Due to the lack of timely inspection and reparation for the pavement cracks, with the development of cracks, the safety and service life of the pavement can be decreased. To curb the development [...] Read more.
Pavement cracks can result in the degradation of pavement performance. Due to the lack of timely inspection and reparation for the pavement cracks, with the development of cracks, the safety and service life of the pavement can be decreased. To curb the development of pavement cracks, detecting these cracks accurately plays an important role. In this paper, an automatic pavement crack detection method is proposed. For achieving real-time inspection, the YOLOV5 was selected as the base model. Due to the small size of the pavement cracks, the accuracy of most of the pavement crack deep learning-based methods cannot reach a high degree. To further improve the accuracy of those kind of methods, attention modules were employed. Based on the self-building datasets collected in Linyi city, the performance among various crack detection models was evaluated. The results showed that adding attention modules can effectively enhance the ability of crack detection. The precision of YOLOV5-CoordAtt reaches 95.27%. It was higher than other conventional and deep learning methods. According to the pictures of the results, the proposed methods can detect accurately under various situations. Full article
(This article belongs to the Special Issue Deep Learning Based Object Detection II)
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13 pages, 3748 KiB  
Article
Small Object Detection Method Based on Weighted Feature Fusion and CSMA Attention Module
by Chao Peng, Meng Zhu, Honge Ren and Mahmoud Emam
Electronics 2022, 11(16), 2546; https://doi.org/10.3390/electronics11162546 - 15 Aug 2022
Cited by 3 | Viewed by 1663
Abstract
Small object detection is one of the challenging tasks in computer vision. Most of the existing small object detection models cannot fully extract the characteristics of small objects within an image, due to the small coverage area, low resolution and unclear detailed information [...] Read more.
Small object detection is one of the challenging tasks in computer vision. Most of the existing small object detection models cannot fully extract the characteristics of small objects within an image, due to the small coverage area, low resolution and unclear detailed information of small objects in the image; hence, the effect of these models is not ideal. To solve this problem, a simple and efficient reinforce feature pyramid network R-FPN is proposed for the YOLOv5 algorithm. The learnable weight is introduced to show the importance of different input features, make full use of the useful information of different feature layers and strengthen the extraction of small object features. At the same time, a channel space mixed attention CSMA module is proposed to extract the detailed information of small objects combined with spaces and channels, suppress other useless information and further improve the accuracy of small object detection. The experimental results show that the proposed method improves the average accuracy AP, AP50 and AR100 of the original algorithm by 2.11%, 2.86% and 1.94%, respectively, and the detection effect is better than the existing small object detection algorithms, which proves the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Deep Learning Based Object Detection II)
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15 pages, 2693 KiB  
Article
Real-Time Automatic Investigation of Indian Roadway Animals by 3D Reconstruction Detection Using Deep Learning for R-3D-YOLOv3 Image Classification and Filtering
by Sudhakar Sengan, Ketan Kotecha, Indragandhi Vairavasundaram, Priya Velayutham, Vijayakumar Varadarajan, Logesh Ravi and Subramaniyaswamy Vairavasundaram
Electronics 2021, 10(24), 3079; https://doi.org/10.3390/electronics10243079 - 10 Dec 2021
Cited by 9 | Viewed by 2874
Abstract
Statistical reports say that, from 2011 to 2021, more than 11,915 stray animals, such as cats, dogs, goats, cows, etc., and wild animals were wounded in road accidents. Most of the accidents occurred due to negligence and doziness of drivers. These issues can [...] Read more.
Statistical reports say that, from 2011 to 2021, more than 11,915 stray animals, such as cats, dogs, goats, cows, etc., and wild animals were wounded in road accidents. Most of the accidents occurred due to negligence and doziness of drivers. These issues can be handled brilliantly using stray and wild animals-vehicle interaction and the pedestrians’ awareness. This paper briefs a detailed forum on GPU-based embedded systems and ODT real-time applications. ML trains machines to recognize images more accurately than humans. This provides a unique and real-time solution using deep-learning real 3D motion-based YOLOv3 (DL-R-3D-YOLOv3) ODT of images on mobility. Besides, it discovers methods for multiple views of flexible objects using 3D reconstruction, especially for stray and wild animals. Computer vision-based IoT devices are also besieged by this DL-R-3D-YOLOv3 model. It seeks solutions by forecasting image filters to find object properties and semantics for object recognition methods leading to closed-loop ODT. Full article
(This article belongs to the Special Issue Deep Learning Based Object Detection II)
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16 pages, 949 KiB  
Article
Correspondence Learning for Deep Multi-Modal Recognition and Fraud Detection
by Jongchan Park, Min-Hyun Kim and Dong-Geol Choi
Electronics 2021, 10(7), 800; https://doi.org/10.3390/electronics10070800 - 28 Mar 2021
Cited by 3 | Viewed by 2683
Abstract
Deep learning-based methods have achieved good performance in various recognition benchmarks mostly by utilizing single modalities. As different modalities contain complementary information to each other, multi-modal based methods are proposed to implicitly utilize them. In this paper, we propose a simple technique, called [...] Read more.
Deep learning-based methods have achieved good performance in various recognition benchmarks mostly by utilizing single modalities. As different modalities contain complementary information to each other, multi-modal based methods are proposed to implicitly utilize them. In this paper, we propose a simple technique, called correspondence learning (CL), which explicitly learns the relationship among multiple modalities. The multiple modalities in the data samples are randomly mixed among different samples. If the modalities are from the same sample (not mixed), then they have positive correspondence, and vice versa. CL is an auxiliary task for the model to predict the correspondence among modalities. The model is expected to extract information from each modality to check correspondence and achieve better representations in multi-modal recognition tasks. In this work, we first validate the proposed method in various multi-modal benchmarks including CMU Multimodal Opinion-Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) sentiment analysis datasets. In addition, we propose a fraud detection method using the learned correspondence among modalities. To validate this additional usage, we collect a multi-modal dataset for fraud detection using real-world samples for reverse vending machines. Full article
(This article belongs to the Special Issue Deep Learning Based Object Detection II)
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15 pages, 3846 KiB  
Article
An Improved Approach for Object Proposals Generation
by Yao Deng, Huawei Liang and Zhiyan Yi
Electronics 2021, 10(7), 794; https://doi.org/10.3390/electronics10070794 - 27 Mar 2021
Cited by 1 | Viewed by 1596
Abstract
The objectness measure is a significant and effective method used for generic object detection. However, several object detection methods can achieve accurate results by using more than 1000 candidate object proposals. In addition, the weight of each proposal is weak and also cannot [...] Read more.
The objectness measure is a significant and effective method used for generic object detection. However, several object detection methods can achieve accurate results by using more than 1000 candidate object proposals. In addition, the weight of each proposal is weak and also cannot distinguish object proposals. These weak proposals have brought difficulties to the subsequent analysis. To significantly reduce the number of proposals, this paper presents an improved generic object detection approach, which predicts candidate object proposals from more than 10,000 proposals. All candidate proposals can be divided, rather than preclassified, into three categories: entire object, partial object, and nonobject. These partial object proposals also display fragmentary information of the objectness feature, which can be used to reconstruct the object boundary. By using partial objectness to enhance the weight of the entire object proposals, we removed a huge number of useless proposals and reduced the space occupied by the true positive object proposals. We designed a neural network with lightweight computation to cluster the most possible object proposals with rerank and box regression. Through joint training, the lightweight network can share the features with other subsequent tasks. The proposed method was validated using experiments with the PASCAL VOC2007 dataset. The results showed that the proposed approach was significantly improved compared with the existing methods and can accurately detect 92.3% of the objects by using less than 200 proposals. Full article
(This article belongs to the Special Issue Deep Learning Based Object Detection II)
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