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Computer Vision for Smart Cities

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

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 15574

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Special Issue Editors

Dr. Luca Ciampi

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Guest Editor

Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Via G. Moruzzi 1, 56124 Pisa, Italy
Interests: computer vision; deep learning; object detection; visual counting; pedestrian detection; unsupervised domain adaptation; image understanding; artificial intelligence

Dr. Nicola Messina

E-Mail Website
Guest Editor

Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Via G. Moruzzi 1, 56124 Pisa, Italy
Interests: CBIR; deep learning; computer vision; transformer networks; abstract reasoning; cross modal; retrieval

Dr. Claudio Gennaro

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Guest Editor

Information Science and Technology Institute (ISTI), Italian National Research Council Department (CNR), Moruzzi 1, 56124 Pisa, Italy
Interests: artificial intelligence; deep learning; information retrieval; similarity search; access methods for multimedia information retrieval; wireless sensor networks
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Special Issue Information

Dear Colleagues,

The ubiquity of video surveillance cameras in modern smart cities and the significant development of artificial intelligence (AI) provide new opportunities for the development of applications and services that make life easier for citizens. Like no other sensing mechanism, city camera networks can ‘observe’ the physical world, while simultaneously providing visual data to AI systems to extract potentially relevant information from this deluge of data and make/suggest decisions which help to solve many real-world problems where humans are at the epicenter. In this context, many smart applications, especially based on deep learning, have been proposed and are nowadays widely employed worldwide, helping to prevent many criminal activities, to plan infrastructures, and to manage public spaces.

This Special Issue aims to collate original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of computer vision for Smart Cities.

Potential topics include, but are not limited to, the following:

pedestrian detection;
people tracking;
parking lot occupancy detection;
face recognition;
human activity monitoring;
person re-identification;
traffic density estimation;
crowd counting;
video violence detection;
human pose estimation;
human action recognition;
personal protective equipment detection;
facial expression recognition;
vehicle tracking;
multi-camera multi-target vehicle tracking.

Dr. Luca Ciampi
Dr. Nicola Messina
Dr. Claudio Gennaro
Guest Editors

Manuscript Submission Information

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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

computer vision
smart city
artificial intelligence
deep learning
image and video analysis
smart cameras
video and image understanding

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Published Papers (3 papers)

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Open AccessArticle

Parking Lot Occupancy Detection with Improved MobileNetV3

by Yusufbek Yuldashev, Mukhriddin Mukhiddinov, Akmalbek Bobomirzaevich Abdusalomov, Rashid Nasimov and Jinsoo Cho

Sensors 2023, 23(17), 7642; https://doi.org/10.3390/s23177642 - 3 Sep 2023

Cited by 7 | Viewed by 3157 | Correction

Abstract

In recent years, parking lot management systems have garnered significant research attention, particularly concerning the application of deep learning techniques. Numerous approaches have emerged for tackling parking lot occupancy challenges using deep learning models. This study contributes to the field by addressing a critical aspect of parking lot management systems: accurate vehicle occupancy determination in specific parking spaces. We propose an advanced solution by harnessing an optimized MobileNetV3 model with custom architectural enhancements, trained on the CNRPark-EXT and PKLOT datasets. The model processes individual parking space patches from real-time video feeds, providing occupancy classification for each patch, identifying occupied or available spaces. Our architectural modifications include the integration of a convolutional block attention mechanism in place of the native attention module and the adoption of blueprint separable convolutions instead of the traditional depth-wise separable convolutions. In terms of performance, our proposed model exhibits superior results when benchmarked against state-of-the-art methods. Achieving an exceptional area under the ROC curve (AUC) value of 0.99 for most experiments with the PKLot dataset, our enhanced MobileNetV3 showcases its exceptional discriminatory power in binary classification. Benchmarked against the CarNet and mAlexNet models, representative of previous state-of-the-art solutions, our proposed model showcases exceptional performance. During evaluations using the combined CNRPark-EXT and PKLot datasets, the proposed model attains an impressive average accuracy of 98.01%, while CarNet achieves 97.03%. Beyond achieving high accuracy and precision comparable to previous models, the proposed model exhibits promise for real-time applications. This work contributes to the advancement of parking lot occupancy detection by offering a robust and efficient solution with implications for urban mobility enhancement and resource optimization. Full article

(This article belongs to the Special Issue Computer Vision for Smart Cities)

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22 pages, 2959 KiB

Open AccessArticle

IDOD-YOLOV7: Image-Dehazing YOLOV7 for Object Detection in Low-Light Foggy Traffic Environments

by Yongsheng Qiu, Yuanyao Lu, Yuantao Wang and Haiyang Jiang

Sensors 2023, 23(3), 1347; https://doi.org/10.3390/s23031347 - 25 Jan 2023

Cited by 35 | Viewed by 11102

Abstract

Convolutional neural network (CNN)-based autonomous driving object detection algorithms have excellent detection results on conventional datasets, but the detector performance can be severely degraded in low-light foggy weather environments. Existing methods have difficulty in achieving a balance between low-light image enhancement and object detection. To alleviate this problem, this paper proposes a foggy traffic environment object detection framework, IDOD-YOLOV7. This network is based on joint optimal learning of image defogging module IDOD (AOD + SAIP) and YOLOV7 detection modules. Specifically, for low-light foggy images, we propose to improve the image quality by joint optimization of image defogging (AOD) and image enhancement (SAIP), where the parameters of the SAIP module are predicted by a miniature CNN network and the AOD module performs image defogging by optimizing the atmospheric scattering model. The experimental results show that the IDOD module not only improves the image defogging quality for low-light fog images but also achieves better results in objective evaluation indexes such as PSNR and SSIM. The IDOD and YOLOV7 learn jointly in an end-to-end manner so that object detection can be performed while image enhancement is executed in a weakly supervised manner. Finally, a low-light fogged traffic image dataset (FTOD) was built by physical fogging in order to solve the domain transfer problem. The training of IDOD-YOLOV7 network by a real dataset (FTOD) improves the robustness of the model. We performed various experiments to visually and quantitatively compare our method with several state-of-the-art methods to demonstrate its superiority over the others. The IDOD-YOLOV7 algorithm not only suppresses the artifacts of low-light fog images and improves the visual effect of images but also improves the perception of autonomous driving in low-light foggy environments. Full article

(This article belongs to the Special Issue Computer Vision for Smart Cities)

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