scholarly journals DC-YOLOv3: A novel efficient object detection algorithm

2021 ◽  
Vol 2082 (1) ◽  
pp. 012012
Author(s):  
Xu Zhang ◽  
Fang Han ◽  
Ping Wang ◽  
Wei Jiang ◽  
Chen Wang
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Real World ◽  
Network Architecture ◽  
Detection Algorithm ◽  
Training Time ◽  
Feature Representations ◽  
Multi Scale ◽  
Time Performance ◽  
Real World Applications

Abstract Feature pyramids have become an essential component in most modern object detectors, such as Mask RCNN, YOLOv3, RetinaNet. In these detectors, the pyramidal feature representations are commonly used which represent an image with multi-scale feature layers. However, the detectors can’t be used in many real world applications which require real time performance under a computationally limited circumstance. In the paper, we study network architecture in YOLOv3 and modify the classical backbone--darknet53 of YOLOv3 by using a group of convolutions and dilated convolutions (DC). Then, a novel one-stage object detection network framework called DC-YOLOv3 is proposed. A lot of experiments on the Pascal 2017 benchmark prove the effectiveness of our framework. The results illustrate that DC-YOLOv3 achieves comparable results with YOLOv3 while being about 1.32× faster in training time and 1.38× faster in inference time.

scholarly journals Real-Time Smile Detection using Deep Learning

2019 ◽  
Vol 35 (2) ◽  
pp. 135-145
Author(s):  
Chi Cuong Nguyen ◽  
Giang Son Tran ◽  
Thi Phuong Nghiem ◽  
Jean-Christophe Burie ◽  
Chi Mai Luong
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Network Architecture ◽  
State Of The Art ◽  
Neural Network Architecture ◽  
Time Performance ◽  
Art Object ◽  
Real World Applications ◽  
Facial Images ◽  
Smile Detection

Real-time smile detection from facial images is useful in many real world applications such as automatic photo capturing in mobile phone cameras or interactive distance learning. In this paper, we study different architectures of object detection deep networks for solving real-time smile detection problem. We then propose a combination of a lightweight convolutional neural network architecture (BKNet) with an efficient object detection framework (RetinaNet). The evaluation on the two datasets (GENKI-4K, UCF Selfie) with a mid-range hardware device (GTX TITAN Black) show that our proposed method helps in improving both accuracy and inference time of the original RetinaNet to reach real-time performance. In comparison with the state-of-the-art object detection framework (YOLO), our method has higher inference time, but still reaches real-time performance and obtains higher accuracy of smile detection on both experimented datasets.

scholarly journals Eye centre localisation: an unsupervised modular approach

Sensor Review ◽  
2016 ◽  
Vol 36 (3) ◽  
pp. 277-286 ◽  
Author(s):  
Wenhao Zhang ◽  
Melvyn Lionel Smith ◽  
Lyndon Neal Smith ◽  
Abdul Rehman Farooq
Keyword(s):  
Real Time ◽  
Real World ◽  
Point Of View ◽  
Modular Approach ◽  
Content Type ◽  
Time Performance ◽  
Wide Range ◽  
Real World Applications ◽  
Coarse To Fine ◽  
Local Levels

Purpose This paper aims to introduce an unsupervised modular approach for eye centre localisation in images and videos following a coarse-to-fine, global-to-regional scheme. The design of the algorithm aims at excellent accuracy, robustness and real-time performance for use in real-world applications. Design/methodology/approach A modular approach has been designed that makes use of isophote and gradient features to estimate eye centre locations. This approach embraces two main modalities that progressively reduce global facial features to local levels for more precise inspections. A novel selective oriented gradient (SOG) filter has been specifically designed to remove strong gradients from eyebrows, eye corners and self-shadows, which sabotage most eye centre localisation methods. The proposed algorithm, tested on the BioID database, has shown superior accuracy. Findings The eye centre localisation algorithm has been compared with 11 other methods on the BioID database and six other methods on the GI4E database. The proposed algorithm has outperformed all the other algorithms in comparison in terms of localisation accuracy while exhibiting excellent real-time performance. This method is also inherently robust against head poses, partial eye occlusions and shadows. Originality/value The eye centre localisation method uses two mutually complementary modalities as a novel, fast, accurate and robust approach. In addition, other than assisting eye centre localisation, the SOG filter is able to resolve general tasks regarding the detection of curved shapes. From an applied point of view, the proposed method has great potentials in benefiting a wide range of real-world human-computer interaction (HCI) applications.

scholarly journals Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5080
Author(s):  
Baohua Qiang ◽  
Ruidong Chen ◽  
Mingliang Zhou ◽  
Yuanchao Pang ◽  
Yijie Zhai ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Image Data ◽  
Semantic Segmentation ◽  
Image Understanding ◽  
Detection Algorithm ◽  
Vital Role ◽  
Multi Scale ◽  
Segmentation Task ◽  
High Level

In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In this paper, we propose an object detection algorithm by jointing semantic segmentation (SSOD) for images. First, we construct a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information. Second, the semantic segmentation task is used as an auxiliary task to allow the algorithm to perform multi-task learning. Finally, multi-scale features are used to predict the location and category of the object. The experimental results show that our algorithm substantially enhances object detection performance and consistently outperforms other three comparison algorithms, and the detection speed can reach real-time, which can be used for real-time detection.

Multi-scale Object Detection Algorithm in Smart City Based on Mixed Dilated Convolution Pyramid

2021 ◽  
Author(s):  
Kangning Yin ◽  
Jie Liang ◽  
Shaoqi Hou ◽  
Rui Zhu ◽  
Guangqiang Yin ◽  
...  
Keyword(s):  
Object Detection ◽  
Smart City ◽  
Detection Algorithm ◽  
Multi Scale ◽  
Dilated Convolution

scholarly journals 3D Object Detection Algorithm for Panoramic Images With Multi-Scale Convolutional Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171461-171470
Author(s):  
Dianwei Wang ◽  
Yanhui He ◽  
Ying Liu ◽  
Daxiang Li ◽  
Shiqian Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection Algorithm ◽  
3D Object ◽  
Multi Scale ◽  
Panoramic Images ◽  
3D Object Detection

scholarly journals Comparative Performance Analysis of Neural Network Real-Time Object Detections in Different Implementations

2020 ◽  
Vol 226 ◽  
pp. 02020
Author(s):  
Alexey V. Stadnik ◽  
Pavel S. Sazhin ◽  
Slavomir Hnatic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Performance Analysis ◽  
Object Detection ◽  
Real Time ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Comparative Performance

The performance of neural networks is one of the most important topics in the field of computer vision. In this work, we analyze the speed of object detection using the well-known YOLOv3 neural network architecture in different frameworks under different hardware requirements. We obtain results, which allow us to formulate preliminary qualitative conclusions about the feasibility of various hardware scenarios to solve tasks in real-time environments.

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