Smart Street Litter Detection and Classification Based on Faster R-CNN and Edge Computing

2020 ◽  
Vol 30 (04) ◽  
pp. 537-553
Author(s):  
Ping Ping ◽  
Guoyan Xu ◽  
Effendy Kumala ◽  
Jerry Gao
Keyword(s):  
Public Health ◽  
Deep Learning ◽  
Mobile Devices ◽  
Service System ◽  
Environment And Public Health ◽  
City Environment ◽  
Edge Based ◽  
Cleaning Methods ◽  
City Systems ◽  
Deep Learning Model

Cleanliness of city streets has an important impact on city environment and public health. Conventional street cleaning methods involve street sweepers going to many spots and manually confirming if the street needs to be clean. However, this method takes a substantial amount of manual operations for detection and assessment of street’s cleanliness which leads to a high cost for cities. Using pervasive mobile devices and AI technology, it is now possible to develop smart edge-based service system for monitoring and detecting the cleanliness of streets at scale. This paper explores an important aspect of cities — how to automatically analyze street imagery to understand the level of street litter. A vehicle (i.e. trash truck) equipped with smart edge station and cameras is used to collect and process street images in real time. A deep learning model is developed to detect, classify and analyze the diverse types of street litters such as tree branches, leaves, bottles and so on. In addition, two case studies are reported to show its strong potential and effectiveness in smart city systems.

Deep Learning-Based Automated Mole Detection: An Algorithm with Future Implication for Tele-dermatology (Preprint)

2020 ◽  
Author(s):  
Quoc-Viet Tran ◽  
Yen-Po Chin ◽  
Phung-Anh Nguyen ◽  
Ming-Yang Lee ◽  
Hsuan-Chia Yang ◽  
...  
Keyword(s):  
Public Health ◽  
Deep Learning ◽  
Automatic Segmentation ◽  
Confidence Score ◽  
Skin Lesions ◽  
Average Precision ◽  
Health Events ◽  
Bounding Boxes ◽  
Future Implication ◽  
Deep Learning Model

BACKGROUND The automatic segmentation of skin lesions has been reported using the data of dermoscopic images. It is, however, not applicable to real-time detection using a smartphone. OBJECTIVE This study aims to examine a deep learning model for detecting and localizing positions of the mole on the captured images to precisely extract the crop images of the model without any other objects. METHODS The data were collected through public health events in Taiwan between December 2017 and February 2019. All the participants who concerned about the risk of their moles were asked to take the mole-images. Images were then measured and determined the risks by three dermatologists. We labeled the mole position with bounding boxes using the ‘LabelImg’ tool. Two architectures, SSD and Faster-RCNN, have been used to build eight different mole-detection models. The confidence score, intersection over union (IoU), and mean average precision (mAP) with the COCO metrics were used to measure the accuracy of those models. RESULTS 2790-mole images were used for the development and the validation of the models. The Faster-RCNN Inception Resnet model had the highest overall mAP of 0.245, following by 0.234 of the Faster-RCNN Resnet 101, and 0.227 of the Faster-RCNN Resnet 50 model. The SSD Mobilenet v1 model had the lowest mAP of 0.142. The Faster-RCNN Inception Resnet model had a dominant AP of 0.377, 0.236, and 0.129 for the large, medium, and small size of moles. We observed that the Faster RCNN Inception Resnet has shown the best performance with the high confident scores (over 97%) for all kinds of moles. CONCLUSIONS We successfully developed the detection models based on the techniques of SSD and Faster-RCNN. These models might help researchers to localize accurately the position of the moles with its risks as a feasible detection app on the smartphone. We provided the pre-trained models for further studies via GitHub link, https://github.com/vietdaica/Mole_Detection.

scholarly journals Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User’s Location

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1227
Author(s):  
Seung-Taek Oh ◽  
Deog-Hyeon Ga ◽  
Jae-Hyun Lim
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
Mobile Device ◽  
Local Level ◽  
Learning Model ◽  
Learning System ◽  
Ultraviolet Rays ◽  
Optimum Number ◽  
Data Set ◽  
Deep Learning Model

Ultraviolet rays are closely related with human health and, recently, optimum exposure to the UV rays has been recommended, with growing importance being placed on correct UV information. However, many countries provide UV information services at a local level, which makes it impossible for individuals to acquire user-based, accurate UV information unless individuals operate UV measurement devices with expertise on the relevant field for interpretation of the measurement results. There is a limit in measuring ultraviolet rays’ information by the users at their respective locations. Research about how to utilize mobile devices such as smartphones to overcome such limitation is also lacking. This paper proposes a mobile deep learning system that calculates UVI based on the illuminance values at the user’s location obtained with mobile devices’ help. The proposed method analyzed the correlation between illuminance and UVI based on the natural light DB collected through the actual measurements, and the deep learning model’s data set was extracted. After the selection of the input variables to calculate the correct UVI, the deep learning model based on the TensorFlow set with the optimum number of layers and number of nodes was designed and implemented, and learning was executed via the data set. After the data set was converted to the mobile deep learning model to operate under the mobile environment, the converted data were loaded on the mobile device. The proposed method enabled providing UV information at the user’s location through a mobile device on which the illuminance sensors were loaded even in the environment without UVI measuring equipment. The comparison of the experiment results with the reference device (spectrometer) proved that the proposed method could provide UV information with an accuracy of 90–95% in the summers, as well as in winters.

scholarly journals Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4592
Author(s):  
Xin Zeng ◽  
Xiaomei Zhang ◽  
Shuqun Yang ◽  
Zhicai Shi ◽  
Chihung Chi
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
User Authentication ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Edge Computing ◽  
Behavior Modeling ◽  
Security And Privacy ◽  
Positive Rate ◽  
Edge Based

Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

scholarly journals Knowledge discovery and dengue forecasting applied in a four-year dataset collected at Natal, RN – Brazil

2021 ◽  
Author(s):  
Ignacio Sanchez-Gendriz ◽  
Gustavo de Souza ◽  
Ion de Andrade ◽  
Adrião Duarte Doria Neto ◽  
Alessandre Tavares ◽  
...  
Keyword(s):  
Public Health ◽  
Socioeconomic Status ◽  
Deep Learning ◽  
Aedes Aegypti ◽  
Health Interventions ◽  
Dengue Vector ◽  
Vector Surveillance ◽  
Public Health Interventions ◽  
Spatially Distributed ◽  
Deep Learning Model

Abstract Dengue, a disease recognized as a health problem, causes significant impacts on health and affects millions of people each year worldwide. A suitable method for dengue vector surveillance is to count eggs the mosquitoes Aedes aegypti have laid in spatially distributed ovitraps. In view of this approach, this study uses a database collected in 397 ovitraps distributed across the municipality of Natal, RN – Brazil. The number of eggs in each ovitrap was counted weekly, over four years (2016 - 2019), and simultaneously analyzed with the incidence of dengue. Our results confirm that dengue incidence seems to be related to socioeconomic status in Natal. Using a deep learning model, we then predict the incidence of new dengue cases based on data obtained from the previous week of dengue or the number of eggs present in the ovitraps. The analysis shows that ovitrap data allows earlier detection (four to six weeks) when compared to dengue cases themselves (one week). Furthermore, the results confirm that quantifying Aedes aegypti eggs may be valuable for planning actions and public health interventions.

scholarly journals Design of Deep Learning-based Technology for Place Image Collecting

2021 ◽  
Vol 12 (5) ◽  
pp. 216-222
Author(s):  
Jin-wook Jang
Keyword(s):  
Deep Learning ◽  
Performance Test ◽  
Research Study ◽  
Service System ◽  
Location Information ◽  
Learning Technology ◽  
Not Given ◽  
Place Image ◽  
Further Development ◽  
Deep Learning Model

This research study designed a location image collecting technology. It provides the exact location information of an image which is not given in the photo to the user. Deep learning technology analysis and collects the images. The purpose of this service system is to provide the exact place name, location and the various information of the place such as nearby recommended attractions when the user upload the image photo to the service system. Suggested system has a deep learning model that has a size of 25.3MB, and the model repeats the learning process 50 times with a total of 15,266 data, performing 93.75% of the final accuracy. In a performance test, the final accuracy of the model is calculated 93.75%. This system can also be linked with various services potentially for further development.  

scholarly journals Accelerating On-Device Learning with Layer-Wise Processor Selection Method on Unified Memory

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2364
Author(s):  
Donghee Ha ◽  
Mooseop Kim ◽  
KyeongDeok Moon ◽  
Chi Yoon Jeong
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
Network Model ◽  
Mobile Device ◽  
Learning Model ◽  
Selection Method ◽  
Superior Performance ◽  
Processing Unit ◽  
Average Power Consumption ◽  
Deep Learning Model

Recent studies have applied the superior performance of deep learning to mobile devices, and these studies have enabled the running of the deep learning model on a mobile device with limited computing power. However, there is performance degradation of the deep learning model when it is deployed in mobile devices, due to the different sensors of each device. To solve this issue, it is necessary to train a network model specific to each mobile device. Therefore, herein, we propose an acceleration method for on-device learning to mitigate the device heterogeneity. The proposed method efficiently utilizes unified memory for reducing the latency of data transfer during network model training. In addition, we propose the layer-wise processor selection method to consider the latency generated by the difference in the processor performing the forward propagation step and the backpropagation step in the same layer. The experiments were performed on an ODROID-XU4 with the ResNet-18 model, and the experimental results indicate that the proposed method reduces the latency by at most 28.4% compared to the central processing unit (CPU) and at most 21.8% compared to the graphics processing unit (GPU). Through experiments using various batch sizes to measure the average power consumption, we confirmed that device heterogeneity is alleviated by performing on-device learning using the proposed method.

Sludge Disposal to Sea in South Africa

1982 ◽  
Vol 14 (3) ◽  
pp. 93-107 ◽  
Author(s):  
D C Macleod
Keyword(s):  
Public Health ◽  
South Africa ◽  
Marine Environment ◽  
Industrial Waste ◽  
Domestic Sewage ◽  
Monitoring Programme ◽  
Environment And Public Health ◽  
Research Project ◽  
Sludge Disposal ◽  
Sea Outfalls

The performance of two sea outfalls that have been in operation off the coast of Durban for over 10 years has been monitored for effects on the marine environment and public health. The discharge has been a mixture of domestic sewage and industrial waste from which a large proportion of the sludge has been removed but a 2-year research project, in which the balance of the sludge is also being discharged, has commenced. Performance of the outfalls and details of the monitoring programme are reviewed.

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