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Image Processing and Analysis for Object Detection: 2nd Edition

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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 1541

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Special Issue Editor

Prof. Dr. Kaihua Zhang

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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

Related Special Issue

Image Processing and Analysis for Object Detection in Sensors (21 articles)

Published Papers (4 papers)

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Research

23 pages, 5060 KiB

Open AccessArticle

Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map

by Zhihao Su, Afzan Adam, Mohammad Faidzul Nasrudin and Anton Satria Prabuwono

Sensors 2024, 24(11), 3529; https://doi.org/10.3390/s24113529 - 30 May 2024

Abstract

Region proposal-based detectors, such as Region-Convolutional Neural Networks (R-CNNs), Fast R-CNNs, Faster R-CNNs, and Region-Based Fully Convolutional Networks (R-FCNs), employ a two-stage process involving region proposal generation followed by classification. This approach is effective but computationally intensive and typically slower than proposal-free methods. Therefore, region proposal-free detectors are becoming popular to balance accuracy and speed. This paper proposes a proposal-free, fully convolutional network (PF-FCN) that outperforms other state-of-the-art, proposal-free methods. Unlike traditional region proposal-free methods, PF-FCN can generate a “box map” based on regression training techniques. This box map comprises a set of vectors, each designed to produce bounding boxes corresponding to the positions of objects in the input image. The channel and spatial contextualized sub-network are further designed to learn a “box map”. In comparison to renowned proposal-free detectors such as CornerNet, CenterNet, and You Look Only Once (YOLO), PF-FCN utilizes a fully convolutional, single-pass method. By reducing the need for fully connected layers and filtering center points, the method considerably reduces the number of trained parameters and optimizes the scalability across varying input sizes. Evaluations of benchmark datasets suggest the effectiveness of PF-FCN: the proposed model achieved an mAP of 89.6% on PASCAL VOC 2012 and 71.7% on MS COCO, which are higher than those of the baseline Fully Convolutional One-Stage Detector (FCOS) and other classical proposal-free detectors. The results prove the significance of proposal-free detectors in both practical applications and future research. Full article

(This article belongs to the Special Issue Image Processing and Analysis for Object Detection: 2nd Edition)

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17 pages, 15093 KiB

Open AccessArticle

Towards Stabilized Few-Shot Object Detection with Less Forgetting via Sample Normalization

by Yang Ren, Menglong Yang, Yanqiao Han and Weizheng Li

Sensors 2024, 24(11), 3456; https://doi.org/10.3390/s24113456 - 27 May 2024

Viewed by 259

Abstract

Few-shot object detection is a challenging task aimed at recognizing novel classes and localizing with limited labeled data. Although substantial achievements have been obtained, existing methods mostly struggle with forgetting and lack stability across various few-shot training samples. In this paper, we reveal two gaps affecting meta-knowledge transfer, leading to unstable performance and forgetting in meta-learning-based frameworks. To this end, we propose sample normalization, a simple yet effective method that enhances performance stability and decreases forgetting. Additionally, we apply Z-score normalization to mitigate the hubness problem in high-dimensional feature space. Experimental results on the PASCAL VOC data set demonstrate that our approach outperforms existing methods in both accuracy and stability, achieving up to +4.4 [email protected] and +5.3 mAR in a single run, with +4.8 [email protected] and +5.1 mAR over 10 random experiments on average. Furthermore, our method alleviates the drop in performance of base classes. The code will be released to facilitate future research. Full article

(This article belongs to the Special Issue Image Processing and Analysis for Object Detection: 2nd Edition)

29 pages, 6574 KiB

Open AccessArticle

Semi-TSGAN: Semi-Supervised Learning for Highlight Removal Based on Teacher-Student Generative Adversarial Network

by Yuanfeng Zheng, Yuchen Yan and Hao Jiang

Sensors 2024, 24(10), 3090; https://doi.org/10.3390/s24103090 - 13 May 2024

Viewed by 407

Abstract

Despite recent notable advancements in highlight image restoration techniques, the dearth of annotated data and the lightweight deployment of highlight removal networks pose significant impediments to further advancements in the field. In this paper, to the best of our knowledge, we first propose a semi-supervised learning paradigm for highlight removal, merging the fusion version of a teacher–student model and a generative adversarial network, featuring a lightweight network architecture. Initially, we establish a dependable repository to house optimal predictions as pseudo ground truth through empirical analyses guided by the most reliable No-Reference Image Quality Assessment (NR-IQA) method. This method serves to assess rigorously the quality of model predictions. Subsequently, addressing concerns regarding confirmation bias, we integrate contrastive regularization into the framework to curtail the risk of overfitting on inaccurate labels. Finally, we introduce a comprehensive feature aggregation module and an extensive attention mechanism within the generative network, considering a balance between network performance and computational efficiency. Our experimental evaluations encompass comprehensive assessments on both full-reference and non-reference highlight benchmarks. The results demonstrate conclusively the substantive quantitative and qualitative enhancements achieved by our proposed algorithm in comparison to state-of-the-art methodologies. Full article

(This article belongs to the Special Issue Image Processing and Analysis for Object Detection: 2nd Edition)

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15 pages, 3407 KiB

Open AccessArticle

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 - 9 Apr 2024

Cited by 1 | Viewed by 570

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. 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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