Face Detection Based on Receptive Field Enhanced Multi-Task Cascaded Convolutional Neural Networks

Li, Xiaochao; Yang, Zhenjie; Wu, Hongwei

doi:10.1109/access.2020.3023782

Cited by 39 publications

(9 citation statements)

References 29 publications

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“…This generally compares favorably with other works in literature which were based on different dataset. Li et al [25] proposed a Grey Wolf Optimizer Algorithm for monitoring driver physiological signals, achieving an accuracy of 97.5%, precision of 98.3%, and recall value of 94%. Siddiqui et al [26] proposed a machine learning model for monitoring drowsiness based on respiratory rate detection, achieving an accuracy of 87%, precision of 86%, and recall of 88%.…”

Section: E Discussion Of Resultsmentioning

confidence: 99%

Machine learning based driver monitoring system: A case study for the Kayoola EVS

Ziryawulawo¹,

Kirabo²,

Mwikirize

et al. 2023

SAIEE Afr. Res. J.

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With the ever-growing traffic density, the number of road accidents has continued to increase. Finding solutions to reduce road accidents and improve traffic safety has become a top priority for Kiira Motors Corporation, a Ugandan state-owned automotive company. The company seeks to develop intelligent driver assistance systems for its market entry product, the Kayoola EVS bus. A machine learning-based driver monitoring system that would monitor driver drowsiness and send out an alarm in case drowsiness is detected has been developed in an attempt to reduce drowsiness-related accidents. The system consists of a camera positioned in such a way as to keep track of the driver's face. The camera is interfaced with a Raspberry Pi minicomputer which carries out the computations and analysis and when drowsiness is detected, an alarm is triggered. Dangerous driver behavior including distraction and fatigue has long been recognized as the main contributing factor in traffic accidents. This paper therefore presents the development of a driver monitoring system for the Kayoola Electric City Bus to address the increasing occurrences of road accidents. The machine learningbased driver monitoring system is designed to be non-intrusive with continuous real-time operation.

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Section: E Discussion Of Resultsmentioning

confidence: 99%

Machine learning based driver monitoring system: A case study for the Kayoola EVS

Ziryawulawo¹,

Kirabo²,

Mwikirize

et al. 2023

SAIEE Afr. Res. J.

View full text Add to dashboard Cite

show abstract

“…The P-R curve consists of a precision curve and a recall curve. The P-R curve reflects global performance [ 33 ]. The following equations are used to calculate precision, recall, and mAP:

where

is a false positive example,

is a false negative example,

is the real example,

is a true negative example,

is the average precision of a category; and k is the number of categories.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Sea Cucumber Detection Algorithm Based on Deep Learning

Zhang

Xing

Wang

et al. 2022

Sensors

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The traditional single-shot multiBox detector (SSD) for the recognition process in sea cucumbers has problems, such as an insufficient expression of features, heavy computation, and difficulty in application to embedded platforms. To solve these problems, we proposed an improved algorithm for sea cucumber detection based on the traditional SSD algorithm. MobileNetv1 is selected as the backbone of the SSD algorithm. We increase the feature receptive field by receptive field block (RFB) to increase feature details and location information of small targets. Combined with the attention mechanism, features at different depths are strengthened and irrelevant features are suppressed. The experimental results show that the improved algorithm has better performance than the traditional SSD algorithm. The average precision of the improved algorithm is increased by 5.1%. The improved algorithm is also more robust. Compared with YOLOv4 and the Faster R-CNN algorithm, the performance of this algorithm on the P-R curve is better, indicating that the performance of this algorithm is better. Thus, the improved algorithm can stably detect sea cucumbers in real time and provide reliable feedback information.

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“…The framework fuses the feature maps of different branches for detecting hard small faces. [17] introduces a receptive field block to extract the robust feature map. Combined with cascaded CNN, it achieves continuous improvements on multiple related dataset.…”

Section: A Face Detectionmentioning

confidence: 99%

A Novel Detection Framework About Conditions of Wearing Face Mask for Helping Control the Spread of COVID-19

et al. 2021

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Properly wearing a face mask has become an effective way to limit the COVID-19 transmission. In this work, we target at detecting the fine-grained wearing state of face mask: face without mask, face with wrong mask, face with correct mask. This task has two main challenging points: 1) absence of practical datasets, and 2) small intra-class distance and large inter-class distance. For the first challenging point, we introduce a new practical dataset covering various conditions, which contains 8635 faces with different wearing status. For the second challenging point, we propose a novel detection framework about conditions of wearing face mask, named Context-Attention R-CNN, which enlarge the intra-class distance and shorten inter-class distance by extracting distinguishing features. Specifically, we first extract the multiple context feature for region proposals, and use attention module to weight these context feature from channel and spatial levels. And then, we decoupling the classification and localization branches to extract more appropriate feature for these two tasks respectively. Experiments show that the Context-Attention R-CNN achieves 84.1% mAP on our proposed dataset, outperforming Faster R-CNN by 6.8 points. Moreover, Context-Attention R-CNN still exceed some state-of-the-art single-stage detectors.

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Face Detection Based on Receptive Field Enhanced Multi-Task Cascaded Convolutional Neural Networks

Cited by 39 publications

References 29 publications

Machine learning based driver monitoring system: A case study for the Kayoola EVS

Machine learning based driver monitoring system: A case study for the Kayoola EVS

Sea Cucumber Detection Algorithm Based on Deep Learning

A Novel Detection Framework About Conditions of Wearing Face Mask for Helping Control the Spread of COVID-19

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