scholarly journals Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Junping Hu ◽  
Shitu Abubakar ◽  
Shengjun Liu ◽  
Xiaobiao Dai ◽  
Gen Yang ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Near Infrared ◽  
Feature Fusion ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Vital Role ◽  
Image Features ◽  
Fusion Technique ◽  
Detection Model ◽  
Road Marking

Pedestrians, motorist, and cyclist remain the victims of poor vision and negligence of human drivers, especially in the night. Millions of people die or sustain physical injury yearly as a result of traffic accidents. Detection and recognition of road markings play a vital role in many applications such as traffic surveillance and autonomous driving. In this study, we have trained a nighttime road-marking detection model using NIR camera images. We have modified the VGG-16 base network of the state-of-the-art faster R-CNN algorithm by using a multilayer feature fusion technique. We have demonstrated another promising feature fusion technique of concatenating all the convolutional layers within a stage to extract image features. The modification boosts the overall detection performance of the model by utilizing the advantages of the shallow layers and the deep layers of the VGG-16 network. The training samples were augmented using random rotation and translation to enhance the heterogeneity of the detection algorithm. We have achieved a mean average precision (mAP) of 89.48% and 92.83% for the baseline faster R-CNN and our modified method, respectively.

scholarly journals BLNN: Multiscale Feature Fusion-Based Bilinear Fine-Grained Convolutional Neural Network for Image Classification of Wood Knot Defects

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingyu Gao ◽  
Fei Wang ◽  
Peng Song ◽  
Junyan Liu ◽  
DaWei Qi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Defect Detection ◽  
Feature Fusion ◽  
Image Features ◽  
Optical Image ◽  
Training Time ◽  
Fine Grained ◽  
Detection Model ◽  
Defect Recognition

Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves the wood utilization. The traditional neural network technique is unemployed for the wood defect detection of optical image used, which results from a long training time, low recognition accuracy, and nonautomatic extraction of defect image features. In this paper, a wood knot defect detection model (so-called BLNN) combined deep learning is reported. Two subnetworks composed of convolutional neural networks are trained by Pytorch. By using the feature extraction capabilities of the two subnetworks and combining the bilinear join operation, the fine-grained features of the image are obtained. The experimental results show that the accuracy has reached up 99.20%, and the training time is obviously reduced with the speed of defect detection about 0.0795 s/image. It indicates that BLNN has the ability to improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.

scholarly journals Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection

2019 ◽  
Vol 11 (5) ◽  
pp. 115 ◽  
Author(s):  
Weihuang Liu ◽  
Jinhao Qian ◽  
Zengwei Yao ◽  
Xintao Jiao ◽  
Jiahui Pan
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Feature Fusion ◽  
Facial Feature ◽  
Detection Algorithm ◽  
Facial Features ◽  
Dynamic Features ◽  
Driver Fatigue ◽  
Stream Network ◽  
Fatigue Detection

Road traffic accidents caused by fatigue driving are common causes of human casualties. In this paper, we present a driver fatigue detection algorithm using two-stream network models with multi-facial features. The algorithm consists of four parts: (1) Positioning mouth and eye with multi-task cascaded convolutional neural networks (MTCNNs). (2) Extracting the static features from a partial facial image. (3) Extracting the dynamic features from a partial facial optical flow. (4) Combining both static and dynamic features using a two-stream neural network to make the classification. The main contribution of this paper is the combination of a two-stream network and multi-facial features for driver fatigue detection. Two-stream networks can combine static and dynamic image information, while partial facial images as network inputs can focus on fatigue-related information, which brings better performance. Moreover, we applied gamma correction to enhance image contrast, which can help our method achieve better results, noted by an increased accuracy of 2% in night environments. Finally, an accuracy of 97.06% was achieved on the National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset.

scholarly journals AF-EMS Detector: Improve the Multi-Scale Detection Performance of the Anchor-Free Detector

2021 ◽  
Vol 13 (2) ◽  
pp. 160
Author(s):  
Jiangqiao Yan ◽  
Liangjin Zhao ◽  
Wenhui Diao ◽  
Hongqi Wang ◽  
Xian Sun
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Feature Fusion ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Natural Image ◽  
Detection Model ◽  
Multi Scale ◽  
Feature Pyramid ◽  
Scale Detection

As a precursor step for computer vision algorithms, object detection plays an important role in various practical application scenarios. With the objects to be detected becoming more complex, the problem of multi-scale object detection has attracted more and more attention, especially in the field of remote sensing detection. Early convolutional neural network detection algorithms are mostly based on artificially preset anchor-boxes to divide different regions in the image, and then obtain the prior position of the target. However, the anchor box is difficult to set reasonably and will cause a large amount of computational redundancy, which affects the generality of the detection model obtained under fixed parameters. In the past two years, anchor-free detection algorithm has achieved remarkable development in the field of detection on natural image. However, there is no sufficient research on how to deal with multi-scale detection more effectively in anchor-free framework and use these detectors on remote sensing images. In this paper, we propose a specific-attention Feature Pyramid Network (FPN) module, which is able to generate a feature pyramid, basing on the characteristics of objects with various sizes. In addition, this pyramid suits multi-scale object detection better. Besides, a scale-aware detection head is proposed which contains a multi-receptive feature fusion module and a size-based feature compensation module. The new anchor-free detector can obtain a more effective multi-scale feature expression. Experiments on challenging datasets show that our approach performs favorably against other methods in terms of the multi-scale object detection performance.

scholarly journals Dynamic Object Detection Algorithm Based on Lightweight Shared Feature Pyramid

2021 ◽  
Vol 13 (22) ◽  
pp. 4610
Author(s):  
Li Zhu ◽  
Zihao Xie ◽  
Jing Luo ◽  
Yuhang Qi ◽  
Liman Liu ◽  
...  
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Dynamic Object ◽  
Shared Feature ◽  
Detection Model ◽  
Detection Algorithms ◽  
Adaptive Inference ◽  
Feature Pyramid

Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a lightweight shared feature pyramid is proposed, which performs adaptive inference according to computing resources and the difficulty of samples, greatly improving the efficiency of inference. Specifically, a lightweight shared feature pyramid network and lightweight detection head is proposed to reduce the amount of computation and parameters in the feature fusion part and detection head of the dynamic object detection model. On the PASCAL VOC dataset, under the two conditions of “anytime prediction” and “budgeted batch object detection”, the performance, computation amount and parameter amount are better than the dynamic object detection models constructed by networks such as ResNet, DenseNet and MSDNet.

scholarly journals A Real-Time Object Detector for Autonomous Vehicles Based on YOLOv4

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Wang ◽  
Ziyue Wang ◽  
Zhengwei Xu ◽  
Chi Wang ◽  
Qiang Li ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
High Speed ◽  
Feature Fusion ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Time Operation ◽  
On The Road

Object detection is an important part of autonomous driving technology. To ensure the safe running of vehicles at high speed, real-time and accurate detection of all the objects on the road is required. How to balance the speed and accuracy of detection is a hot research topic in recent years. This paper puts forward a one-stage object detection algorithm based on YOLOv4, which improves the detection accuracy and supports real-time operation. The backbone of the algorithm doubles the stacking times of the last residual block of CSPDarkNet53. The neck of the algorithm replaces the SPP with the RFB structure, improves the PAN structure of the feature fusion module, adds the attention mechanism CBAM and CA structure to the backbone and neck structure, and finally reduces the overall width of the network to the original 3/4, so as to reduce the model parameters and improve the inference speed. Compared with YOLOv4, the algorithm in this paper improves the average accuracy on KITTI dataset by 2.06% and BDD dataset by 2.95%. When the detection accuracy is almost unchanged, the inference speed of this algorithm is increased by 9.14%, and it can detect in real time at a speed of more than 58.47 FPS.

scholarly journals Loop Closure Detection Based on Multi-Scale Deep Feature Fusion

2019 ◽  
Vol 9 (6) ◽  
pp. 1120 ◽  
Author(s):  
Baifan Chen ◽  
Dian Yuan ◽  
Chunfa Liu ◽  
Qian Wu
Keyword(s):  
Feature Fusion ◽  
Receptive Fields ◽  
Detection Algorithm ◽  
Image Features ◽  
Mobile Robot Navigation ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Multi Scale ◽  
Deep Feature ◽  
Illumination Changes

Loop closure detection plays a very important role in the mobile robot navigation field. It is useful in achieving accurate navigation in complex environments and reducing the cumulative error of the robot’s pose estimation. The current mainstream methods are based on the visual bag of word model, but traditional image features are sensitive to illumination changes. This paper proposes a loop closure detection algorithm based on multi-scale deep feature fusion, which uses a Convolutional Neural Network (CNN) to extract more advanced and more abstract features. In order to deal with the different sizes of input images and enrich receptive fields of the feature extractor, this paper uses the spatial pyramid pooling (SPP) of multi-scale to fuse the features. In addition, considering the different contributions of each feature to loop closure detection, the paper defines the distinguishability weight of features and uses it in similarity measurement. It reduces the probability of false positives in loop closure detection. The experimental results show that the loop closure detection algorithm based on multi-scale deep feature fusion has higher precision and recall rates and is more robust to illumination changes than the mainstream methods.

Strain-Multiplexing Optical-Tuning based on Single-Pulsed Holographic Nanostructures

Nanoscale ◽  
2021 ◽  
Author(s):  
Muhammad Waqas Khalid ◽  
Rajib Ahmed ◽  
Haider Butt
Keyword(s):  
Data Storage ◽  
Near Infrared ◽  
Vital Role

Holographic flexible and rigid-nanostructures in the visible to near infrared plays a vital role in various applications including display, data storage, imaging, and security. However, personalized use of holography is...

scholarly journals Upsampling Matters for Road Marking Segmentation of Autonomous Driving

2020 ◽  
Vol 53 (5) ◽  
pp. 232-237
Author(s):  
Ye Liu ◽  
Xi Zhang ◽  
Lei Liu ◽  
Lei Zhang
Keyword(s):  
Autonomous Driving ◽  
Road Marking

scholarly journals Hand Surgery in Thailand

2021 ◽  
Vol 13 (01) ◽  
pp. 35-41
Author(s):  
Sunyarn Niempoog ◽  
Kiat Witoonchart ◽  
Woraphon Jaroenporn
Keyword(s):  
World War Ii ◽  
Traffic Accidents ◽  
Hand Surgery ◽  
Vital Role ◽  
Second Phase ◽  
World War ◽  
Third Phase ◽  
Foreign Countries ◽  
General Surgeons ◽  
Set Up

AbstractModern hand surgery in Thailand started after the end of World War II. It is divided into 4 phases. In the initial phase (1950-1965), the surgery of the hand was mainly performed by general surgeons. In 1965-1975, which was the second phase, many plastic surgeons and orthopaedic surgeons graduated from foreign countries and came back to Thailand. They played a vital role in the treatment of the surgery of the hand and set up hand units in many centers. They also contributed to the establishment of the “Thai Society for Surgery of the Hand,” which still continues to operate. In the third phase (1975-2000), there was a dramatic development of microsurgery because of the rapid economic expansion. There were many replantation, free tissue transfers, and brachial plexus surgeries in traffic and factory-related accidents. The first hand-fellow training program began in 1993. In the fourth phase (since 2000), the number of hand injuries from factory-related accidents began declining. But the injury from traffic accidents had been increasing both in severity and number. Moreover, the diseases of hand that relate to aging and degeneration had been on the rise. Thai hand surgeons have been using several state-of-the-art technologies such as arthroscopic and endoscopic surgery. They are continuing to invent innovations, generating international publications, and frequently being invited as speakers in foreign countries.

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