Real‐Time Human Ear Detection Based on the Joint of Yolo and RetinaFace

Quoc, Huy Nguyen; Hoang, Vinh Truong

doi:10.1155/2021/7918165

Cited by 14 publications

(10 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome these challenges, a cascaded network is introduced in [124] using the Dilation RetinaNet Face Location (DRFL) Network, which helps reduce network parameters and identify faces at different scales. In [125], the authors introduced a new human ear detection pipeline based on the YOLOv3 detector. A well-known face detector named RetinaFace was also added to the detection system to narrow the regions of interest and enhance accuracy.…”

Section: One-stage Fmdmentioning

confidence: 99%

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Himeur

Al-Maadeed

Varlamis

et al. 2023

Systems

View full text Add to dashboard Cite

After different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across the world. To slow the spread of the COVID-19 virus, several measures have been adopted since the start of the outbreak, including wearing face masks and maintaining social distancing. Ensuring safety in public areas of smart cities requires modern technologies, such as deep learning and deep transfer learning, and computer vision for automatic face mask detection and accurate control of whether people wear masks correctly. This paper reviews the progress in face mask detection research, emphasizing deep learning and deep transfer learning techniques. Existing face mask detection datasets are first described and discussed before presenting recent advances to all the related processing stages using a well-defined taxonomy, the nature of object detectors and Convolutional Neural Network architectures employed and their complexity, and the different deep learning techniques that have been applied so far. Moving on, benchmarking results are summarized, and discussions regarding the limitations of datasets and methodologies are provided. Last but not least, future research directions are discussed in detail.

show abstract

Section: One-stage Fmdmentioning

confidence: 99%

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Himeur

Al-Maadeed

Varlamis

et al. 2023

Systems

View full text Add to dashboard Cite

show abstract

“…We use the vgg16 network as the backbone network. As shown in Figure 2, we delete the full C � C (1) , C (2) , C (3) , C (4) , C (5) , C (6) 􏽮 􏽯.…”

Section: Features Extractionmentioning

confidence: 99%

“…F (5) F (3) F (4) F (5) F (6) Conv layer preliminarily fused feature P, which can be expressed as follows:…”

Section: Features Fusion Module (Ffm)mentioning

confidence: 99%

“…The goal of salient object detection (SOD) is to find the most distinct and salient objects in an image. Salient object detection as an important preprocessing task in computer vision applications has been widely applied in many fields, such as semantic segmentation [ 1 , 2 ], video segmentation [ 3 ], object recognition [ 4 , 5 ], and cropping [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MRBENet: A Multiresolution Boundary Enhancement Network for Salient Object Detection

Jia

Dongye

Peng

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Salient Object Detection (SOD) simulates the human visual perception in locating the most attractive objects in the images. Existing methods based on convolutional neural networks have proven to be highly effective for SOD. However, in some cases, these methods cannot satisfy the need of both accurately detecting intact objects and maintaining their boundary details. In this paper, we present a Multiresolution Boundary Enhancement Network (MRBENet) that exploits edge features to optimize the location and boundary fineness of salient objects. We incorporate a deeper convolutional layer into the backbone network to extract high-level semantic features and indicate the location of salient objects. Edge features of different resolutions are extracted by a U-shaped network. We designed a Feature Fusion Module (FFM) to fuse edge features and salient features. Feature Aggregation Module (FAM) based on spatial attention performs multiscale convolutions to enhance salient features. The FFM and FAM allow the model to accurately locate salient objects and enhance boundary fineness. Extensive experiments on six benchmark datasets demonstrate that the proposed method is highly effective and improves the accuracy of salient object detection compared with state-of-the-art methods.

show abstract

“…Xu et al [ 23 ] proposed the SR-YOLOv5 model on the basis of YOLOv5 to improve the model’s feature-extraction capability in relation to human faces, resulting in a face recognition accuracy of 96.3%. Quoc et al [ 24 ] improved the model’s feature-extraction capability when detecting human ears, resulting in an accuracy rate of 98.7%. Hence, this study aims to improve the detection accuracy of YOLOv7 for use in wheat-ear detection studies.…”

Section: Introductionmentioning

confidence: 99%

Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort

Zhong

Chen

et al. 2023

Sensors

View full text Add to dashboard Cite

The number of wheat ears in a field is an important parameter for accurately estimating wheat yield. In a large field, however, it is hard to conduct an automated and accurate counting of wheat ears because of their density and mutual overlay. Unlike the majority of the studies conducted on deep learning-based methods that usually count wheat ears via a collection of static images, this paper proposes a counting method based directly on a UAV video multi-objective tracking method and better counting efficiency results. Firstly, we optimized the YOLOv7 model because the basis of the multi-target tracking algorithm is target detection. Simultaneously, the omni-dimensional dynamic convolution (ODConv) design was applied to the network structure to significantly improve the feature-extraction capability of the model, strengthen the interaction between dimensions, and improve the performance of the detection model. Furthermore, the global context network (GCNet) and coordinate attention (CA) mechanisms were adopted in the backbone network to implement the effective utilization of wheat features. Secondly, this study improved the DeepSort multi-objective tracking algorithm by replacing the DeepSort feature extractor with a modified ResNet network structure to achieve a better extraction of wheat-ear-feature information, and the constructed dataset was then trained for the re-identification of wheat ears. Finally, the improved DeepSort algorithm was used to calculate the number of different IDs that appear in the video, and an improved method based on YOLOv7 and DeepSort algorithms was then created to calculate the number of wheat ears in large fields. The results show that the mean average precision (mAP) of the improved YOLOv7 detection model is 2.5% higher than that of the original YOLOv7 model, reaching 96.2%. The multiple-object tracking accuracy (MOTA) of the improved YOLOv7–DeepSort model reached 75.4%. By verifying the number of wheat ears captured by the UAV method, it can be determined that the average value of an L1 loss is 4.2 and the accuracy rate is between 95 and 98%; thus, detection and tracking methods can be effectively performed, and the efficient counting of wheat ears can be achieved according to the ID value in the video.

show abstract

Real‐Time Human Ear Detection Based on the Joint of Yolo and RetinaFace

Cited by 14 publications

References 60 publications

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

MRBENet: A Multiresolution Boundary Enhancement Network for Salient Object Detection

Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort

Contact Info

Product

Resources

About