Helmet Detection Based On Improved YOLO V3 Deep Model

Wu, Fan; Jin, Guoqing; Gao, Mingyu; He, Zhiwei; Yang, Yuxiang

doi:10.1109/icnsc.2019.8743246

Cited by 74 publications

(21 citation statements)

References 11 publications

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“…YOLO (You Look Once), algorithm proposed by RedMon and Grishick [52], has the ability to detect objects by regression and location exclusion and classification of the object from one end to the other by a single parsing. This makes it position itself at the top for speed algorithms, but with a low degree of accuracy in the case of small objects and an error rate in the case of pedestrian scenes with a high degree of complexity [53,54].…”

Section: Description Pedestrian Setup and Practical Scenariosmentioning

confidence: 99%

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Zadobrischi

Dimian

2021

Remote Sensing

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Vehicle density and technological development increase the need for road and pedestrian safety systems. Identifying problems and addressing them through the development of systems to reduce the number of accidents and loss of life is imperative. This paper proposes the analysis and management of dangerous situations, with the help of systems and modules designed in this direction. The approach and classification of situations that can cause accidents is another feature analyzed in this paper, including detecting elements of a psychosomatic nature: analysis and detection of the conditions a driver goes through, pedestrian analysis, and maintaining a preventive approach, all of which are embedded in a modular architecture. The versatility and usefulness of such a system come through its ability to adapt to context and the ability to communicate with traffic safety systems such as V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure), V2X (vehicle-to-everything), and VLC (visible light communication). All these elements are found in the operation of the system and its ability to become a portable device dedicated to road safety based on (radio frequency) RF-VLC (visible light communication).

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Section: Description Pedestrian Setup and Practical Scenariosmentioning

confidence: 99%

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Zadobrischi

Dimian

2021

Remote Sensing

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“…It can be seen from Tables 6 and 7 that the tracker algorithm is computationally heavier than all other components in the system. The UKF algorithm consists of many approximations and iterations, because of which it is expected to be computationally heavy [49][50][51][52][53].…”

Section: Frame Rate For the Execution Of Ebamentioning

confidence: 99%

“…It can be seen from Table 6 and Table 7 that the tracker algorithm is computationally heavier than all other components in the system. The UKF algorithm consists of many approximations and iterations, because of which it is expected to be computationally heavy [ 49 , 50 , 51 , 52 , 53 ]. Other alternatives, such as the extended Kalman filter, might be computationally lighter but are prone to more errors [ 54 ].…”

Section: Emergency Brake Assist (Eba) Using Ocsf and Odsfmentioning

confidence: 99%

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Deo¹,

Palade

Huda

2021

Sensors

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Many advanced driver assistance systems (ADAS) are currently trying to utilise multi-sensor architectures, where the driver assistance algorithm receives data from a multitude of sensors. As mono-sensor systems cannot provide reliable and consistent readings under all circumstances because of errors and other limitations, fusing data from multiple sensors ensures that the environmental parameters are perceived correctly and reliably for most scenarios, thereby substantially improving the reliability of the multi-sensor-based automotive systems. This paper first highlights the significance of efficiently fusing data from multiple sensors in ADAS features. An emergency brake assist (EBA) system is showcased using multiple sensors, namely, a light detection and ranging (LiDAR) sensor and camera. The architectures of the proposed ‘centralised’ and ‘decentralised’ sensor fusion approaches for EBA are discussed along with their constituents, i.e., the detection algorithms, the fusion algorithm, and the tracking algorithm. The centralised and decentralised architectures are built and analytically compared, and the performance of these two fusion architectures for EBA are evaluated in terms of speed of execution, accuracy, and computational cost. While both fusion methods are seen to drive the EBA application at an acceptable frame rate (~20fps or higher) on an Intel i5-based Ubuntu system, it was concluded through the experiments and analytical comparisons that the decentralised fusion-driven EBA leads to higher accuracy; however, it has the downside of a higher computational cost. The centralised fusion-driven EBA yields comparatively less accurate results, but with the benefits of a higher frame rate and lesser computational cost.

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“…Each grid outputs B bounding prediction boxes, including bounding box location data that consist of coordinates of the middle point (x, y), width (w), height (h), and confidence prediction. The Yolo loss function of the boundary box consists of four sections [37], and the formula could be seen on Equation (1) [38][39][40].…”

Section: Traffic Sign Recognition With You Only Look Once (Yolo) V3mentioning

confidence: 99%

“…(2) The Yolo loss function of the boundary box consists of four sections [37], and the formula could be seen on Equation (1) [38][39][40].…”

Section: Traffic Sign Recognition With You Only Look Once (Yolo) V3mentioning

confidence: 99%

Deep Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis

et al. 2020

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In the area of traffic sign detection (TSD) methods, deep learning has been implemented and achieves outstanding performance. The detection of a traffic sign, as it has a dual function in monitoring and directing the driver, is a big concern for driver support systems. A core feature of autonomous vehicle systems is the identification of the traffic sign. This article focuses on the prohibitive sign. The objective is to detect in real-time and reduce processing time considerably. In this study, we implement the spatial pyramid pooling (SPP) principle to boost Yolo V3’s backbone network for the extraction of functionality. Our work uses SPP for more comprehensive learning of multiscale object features. Then, perform a comparative investigation of Yolo V3 and Yolo V3 SPP across various scales to recognize the prohibitory sign. Comparisons with Yolo V3 SPP models reveal that their mean average precision (mAP) is higher than Yolo V3. Furthermore, the test accuracy findings indicate that the Yolo V3 SPP model performs better than Yolo V3 for different sizes.

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Helmet Detection Based On Improved YOLO V3 Deep Model

Cited by 74 publications

References 11 publications

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Deep Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis

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