CDNet: a real-time and robust crosswalk detection network on Jetson nano based on YOLOv5

Zhang, Zhengde; Tan, Meng-Lu; Lan, Zhi-Cai; Liu, Haichun; Pei, Ling; Wang, Yu

doi:10.1007/s00521-022-07007-9

Cited by 46 publications

(19 citation statements)

References 25 publications

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“…A similar approach was proposed by Saradopoulos et al [ 21 ] that trains a DNN with a properly crafted dataset and then compares the system performance of their different edge devices (i.e., ESP32, Raspberry PI 4, and Google Coral Dev Board) in term of power consumption, accuracy, the processing time, and memory footprint. In the intelligent traffic management domain, Zhang et al [ 22 ] adapt the YOLOv5 network to detect zebra-crossing using images captured by a camera mounted in the front of a car. An NVIDIA Jetson Nano board infers the DNN, and the authors declare that the device can deliver a detection rate of 33.1 FPS with an F1 score higher than 94%.…”

Section: Related Workmentioning

confidence: 99%

An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments

Antonini

Pincheira

Vecchio

et al. 2023

Sensors

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Industrial assets often feature multiple sensing devices to keep track of their status by monitoring certain physical parameters. These readings can be analyzed with machine learning (ML) tools to identify potential failures through anomaly detection, allowing operators to take appropriate corrective actions. Typically, these analyses are conducted on servers located in data centers or the cloud. However, this approach increases system complexity and is susceptible to failure in cases where connectivity is unavailable. Furthermore, this communication restriction limits the approach’s applicability in extreme industrial environments where operating conditions affect communication and access to the system. This paper proposes and evaluates an end-to-end adaptable and configurable anomaly detection system that uses the Internet of Things (IoT), edge computing, and Tiny-MLOps methodologies in an extreme industrial environment such as submersible pumps. The system runs on an IoT sensing Kit, based on an ESP32 microcontroller and MicroPython firmware, located near the data source. The processing pipeline on the sensing device collects data, trains an anomaly detection model, and alerts an external gateway in the event of an anomaly. The anomaly detection model uses the isolation forest algorithm, which can be trained on the microcontroller in just 1.2 to 6.4 s and detect an anomaly in less than 16 milliseconds with an ensemble of 50 trees and 80 KB of RAM. Additionally, the system employs blockchain technology to provide a transparent and irrefutable repository of anomalies.

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Section: Related Workmentioning

confidence: 99%

An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments

Antonini

Pincheira

Vecchio

et al. 2023

Sensors

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“…In typical traditional convolution operations, fixed-size convolutional kernels are employed, and the sensing field is always rectangular, regardless of network depth or width [22]. Because of this constraint, the generalizing capacity of traditional convolution is limited.…”

Section: B Deformable Convolutionalmentioning

confidence: 99%

Investigation Into Recognition Algorithm of Helmet Violation Based on YOLOv5-CBAM-DCN

et al. 2022

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Recognition of safety helmets wearing by construction workers is a common target detection topic in applications of deep learning-based image processing. This paper provides a study of an enhanced YOLOv5-based method, in which the challenges caused by complicated construction environment backgrounds, dense targets, and the irregular shape of safety helmets are addressed. In a trunk network, feature extraction is more based on the target shape by using the Deformable Convolution Net instead of the conventional convolution; in the Neck, a Convolutional Block Attention Module is introduced to weaken feature extraction of complex backgrounds by giving weights to enhance the characterization ability of target features; and the original network's Generalized Intersection over Union Loss is replaced by Distance Intersection over Union Loss to overcome the problem of erroneous location when the population is dense. The dataset for the training network is created by mixing open-source datasets with autonomous collecting to evaluate the effectiveness of the algorithm. We observed that the improved model has a detection accuracy of 91.6%, up 2.3% over the original network model, and a detection speed of 29 frames per second, which is compliant with most security cameras' capture frame rate.

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“…As shown in Figure 7 , the JETSON NANO B01 embedded computer serves as the verification platform for algorithm implementation (Kalms et al, 2017 ; Bao et al, 2022 ; Edel and Kapustin, 2022 ; Zhang et al, 2022 ). The UAV is equipped with a vision computer, and the camera is used as the image acquisition terminal to calculate the features of the real-time image and the benchmark image.…”

Section: Algorithm Embedded Device Verificationmentioning

confidence: 99%

High precision visual localization method of UAV based on feature matching

Xue¹,

Zhong²,

Liao³

et al. 2022

Front. Comput. Neurosci.

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In this paper, the precision hovering problem of UAV operation is studied. Aiming at the diversity and complexity of the UAV operating environment, a high-precision visual positioning and orientation method based on image feature matching was proposed. The image feature matching based on the improved AKAZE algorithm is realized, and the optimal matching point pair screening method based on the fusion of Hamming distance and matching line angle is innovatively proposed, which greatly improves the robustness of the algorithm without affecting the performance of the algorithm. The real-time image is matched with the benchmark image for image feature matching. By reducing the deviation of image feature, the pose state correction of UAV hovering is achieved, and the precision hovering of the UAV is realized. Both simulation and real UAV tests verify the effectiveness of the proposed UAV high-precision visual positioning and orientation method.

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CDNet: a real-time and robust crosswalk detection network on Jetson nano based on YOLOv5

Cited by 46 publications

References 25 publications

An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments

An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments

Investigation Into Recognition Algorithm of Helmet Violation Based on YOLOv5-CBAM-DCN

High precision visual localization method of UAV based on feature matching

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