Real-time detection of road manhole covers with a deep learning model

Pang, Dangfeng; Guan, Zhiwei; Luo, Tao; Su, Wei; Dou, Ruzhen

doi:10.1038/s41598-023-43173-z

Cited by 7 publications

(3 citation statements)

References 32 publications

(32 reference statements)

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“…Recently, it was reported that the model performance is superior with ultrasound videos compared to static ultrasound images [22]. YOLOv7, used in our study, and the latest version, YOLOv8 [55], are the fastest and most accurate real-time object detection models, which are suitable for multiple object detection from video files in real time. The development of a YOLO-based model that can detect metastatic LNs in real time during US examinations will be one of the most important themes of future research, necessitating multicenter studies with vast quantities of US video data.…”

Section: Discussionmentioning

confidence: 91%

Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma

Eida,

Fukuda,

Katayama

et al. 2024

Cancers

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Ultrasonography is the preferred modality for detailed evaluation of enlarged lymph nodes (LNs) identified on computed tomography and/or magnetic resonance imaging, owing to its high spatial resolution. However, the diagnostic performance of ultrasonography depends on the examiner’s expertise. To support the ultrasonographic diagnosis, we developed YOLOv7-based deep learning models for metastatic LN detection on ultrasonography and compared their detection performance with that of highly experienced radiologists and less experienced residents. We enrolled 462 B- and D-mode ultrasound images of 261 metastatic and 279 non-metastatic histopathologically confirmed LNs from 126 patients with head and neck squamous cell carcinoma. The YOLOv7-based B- and D-mode models were optimized using B- and D-mode training and validation images and their detection performance for metastatic LNs was evaluated using B- and D-mode testing images, respectively. The D-mode model’s performance was comparable to that of radiologists and superior to that of residents’ reading of D-mode images, whereas the B-mode model’s performance was higher than that of residents but lower than that of radiologists on B-mode images. Thus, YOLOv7-based B- and D-mode models can assist less experienced residents in ultrasonographic diagnoses. The D-mode model could raise the diagnostic performance of residents to the same level as experienced radiologists.

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Section: Discussionmentioning

confidence: 91%

Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma

Eida,

Fukuda,

Katayama

et al. 2024

Cancers

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“…For example, Wei et al [11] used a combination of multiple symmetrically arranged cameras and lidars to classify manhole covers through descriptors and support vector machine algorithms. Pang et al [12] proposed a real-time road manhole cover detection method based on deep learning model. By optimizing the network structure and reducing the size of the model and the number of parameters, the effect of deployment on vehicle-mounted embedded devices is achieved.…”

Section: Introductionmentioning

confidence: 99%

Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery

Wang,

Huang

2024

Applied Sciences

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Urban underground pipeline networks are a key component of urban infrastructure, and a large number of older urban areas lack information about their underground pipelines. In addition, survey methods for underground pipelines are often time-consuming and labor-intensive. While the manhole cover serves as the hub connecting the underground pipe network with the ground, the generation of underground pipe network can be realized by obtaining the location and category information of the manhole cover. Therefore, this paper proposed a manhole cover detection method based on UAV aerial photography to obtain ground images, using image super-resolution reconstruction and image positioning and classification. Firstly, the urban image was obtained by UAV aerial photography, and then the YOLOv8 object detection technology was used to accurately locate the manhole cover. Next, the SRGAN network was used to perform super-resolution processing on the manhole cover text to improve the clarity of the recognition image. Finally, the clear manhole cover text image was input into the VGG16_BN network to realize the manhole cover classification. The experimental results showed that the manhole cover classification accuracy of this paper’s method reached 97.62%, which verified its effectiveness in manhole cover detection. The method significantly reduces the time and labor cost and provides a new method for manhole cover information acquisition.

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“…Recently, deep learning and edge computing have become popular for real-time obstacle detection and tracking, offering improved accuracy. Deep learning models such as YOLO, CNNs, and DSODs are especially noted for their efficacy in aerial vehicle obstacle detection [12,13]. However, the success of these models largely depends on the quality and variety of training data.…”

Section: Introductionmentioning

confidence: 99%

YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility

Ywet,

Maw,

Nguyen

et al. 2024

Aerospace

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Urban Air Mobility (UAM) emerges as a transformative approach to address urban congestion and pollution, offering efficient and sustainable transportation for people and goods. Central to UAM is the Operational Digital Twin (ODT), which plays a crucial role in real-time management of air traffic, enhancing safety and efficiency. This study introduces a YOLOTransfer-DT framework specifically designed for Artificial Intelligence (AI) training in simulated environments, focusing on its utility for experiential learning in realistic scenarios. The framework’s objective is to augment AI training, particularly in developing an object detection system that employs visual tasks for proactive conflict identification and mission support, leveraging deep and transfer learning techniques. The proposed methodology combines real-time detection, transfer learning, and a novel mix-up process for environmental data extraction, tested rigorously in realistic simulations. Findings validate the use of existing deep learning models for real-time object recognition in similar conditions. This research underscores the value of the ODT framework in bridging the gap between virtual and actual environments, highlighting the safety and cost-effectiveness of virtual testing. This adaptable framework facilitates extensive experimentation and training, demonstrating its potential as a foundation for advanced detection techniques in UAM.

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Real-time detection of road manhole covers with a deep learning model

Cited by 7 publications

References 32 publications

Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma

Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma

Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery

YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility

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