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Image Processing and Analysis for Object Detection: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 619

Special Issue Editor


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Guest Editor
School of Information and Control, Nanjing University of Information Science and Technology, Nanjing, China
Interests: computer vision; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years have seen an explosion in interest in the development of deep learning techniques for computer vision. As deep learning comes to encompass almost all fields of science and engineering, computer vision remains one of its primary application areas. Specifically, the use of deep learning to handle computer vision tasks has led to numerous unprecedented applications, such as high-accuracy object detection, visual tracking, image segmentation, image/video super-resolution, satellite image processing, and saliency object detection, which cannot achieve promising performance through the use of conventional methods.

This Special Issue aims to cover the latest advances in the field of computer vision, involving the use of sensors (such as cameras, video cameras, drones, etc.) for image acquisition, the use of deep learning methods, and a special focus on low-level and high-level computer vision tasks. Original research and review articles are welcome. Potential topics may include, but are not limited to, the following:

Image/video super-resolution with deep learning approaches;
Object detection, visual tracking, and image/video segmentation with deep learning approaches;
Supervised and unsupervised learning for image/video processing;
Satellite image processing with deep learning techniques;
Low-light image enhancement using deep learning approaches.

Prof. Dr. Kaihua Zhang
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. Sensors 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 2600 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.

Keywords

  • deep learning
  • computer vision
  • object detection
  • visual tracking
  • image super-resolution
  • saliency object detection

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Published Papers (1 paper)

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Research

15 pages, 3407 KiB  
Article
A Lightweight Vehicle Detection Method Fusing GSConv and Coordinate Attention Mechanism
by Deqi Huang, Yating Tu, Zhenhua Zhang and Zikuang Ye
Sensors 2024, 24(8), 2394; https://doi.org/10.3390/s24082394 - 09 Apr 2024
Cited by 1 | Viewed by 424
Abstract
Aiming at the problems of target detection models in traffic scenarios including a large number of parameters, heavy computational burden, and high application cost, this paper introduces an enhanced lightweight real-time detection algorithm, which exhibits higher detection speed and accuracy for vehicle detection. [...] Read more.
Aiming at the problems of target detection models in traffic scenarios including a large number of parameters, heavy computational burden, and high application cost, this paper introduces an enhanced lightweight real-time detection algorithm, which exhibits higher detection speed and accuracy for vehicle detection. This paper considers the YOLOv7 algorithm as the benchmark model, designs a lightweight backbone network, and uses the MobileNetV3 lightweight network to extract target features. Inspired by the structure of SPPF, the spatial pyramid pooling module is reconfigured by incorporating GSConv, and a lightweight SPPFCSPC-GS module is designed, aiming to minimize the quantity of model parameters and enhance the training speed even further. Furthermore, the CA mechanism is integrated to enhance the feature extraction capability of the model. Finally, the MPDIoU loss function is utilized to optimize the model’s training process. Experiments showcase that the refined YOLOv7 algorithm can achieve 98.2% mAP on the BIT-Vehicle dataset with 52.8% fewer model parameters than the original model and a 35.2% improvement in FPS. The enhanced model adeptly strikes a finer equilibrium between velocity and precision, providing favorable conditions for embedding the model into mobile devices. Full article
(This article belongs to the Special Issue Image Processing and Analysis for Object Detection: 2nd Edition)
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