scholarly journals Transferability of a Convolutional Neural Network (CNN) to Measure Traffic Density

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1189
Author(s):  
Jiyong Chung ◽  
Gyeongjun Kim ◽  
Keemin Sohn
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Synthesis Method ◽  
Image Synthesis ◽  
Quadratic Equation ◽  
Road Segment ◽  
Traffic Density ◽  
Minute Amount ◽  
Human Effort ◽  
Bounding Boxes

Whereas detecting individual vehicles in a video image using a convolutional neural network (CNN) prevails for traffic surveillance, CNNs also have been successfully adapted to counting vehicles via a regression method, which conveys the advantages of simplifying the model structure, and inference time can be reduced in the field. This model also demands much less human effort to tag images with labels. The number of vehicles in an image becomes the label, rather than bounding boxes drawn around every single vehicle. Nonetheless, the labeling task takes considerable time whenever a CNN model is trained and tested for a new road segment. There are two ways to alleviate the human effort involved in using this method. A previous study used a pseudo label pre-training method, and another study employed an image synthesis method to solve the problem. Besides these two methods, we investigated the model transferability to reduce the labeling effort. Using a CNN that was fully trained on images of a road segment, we devised a robust way to utilize the trained model for another site by transforming the model output with a simple quadratic equation. The utility of the proposed method was confirmed at the expense of a minute amount of deterioration in accuracy.

scholarly journals Segmentation of Overlapping Cervical Cells with Mask Region Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiajia Chen ◽  
Baocan Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Cytological Analysis ◽  
Segmentation Method ◽  
Challenging Tasks ◽  
Diagnostic Technology ◽  
Cervical Cells ◽  
Bounding Boxes

The task of segmenting cytoplasm in cytology images is one of the most challenging tasks in cervix cytological analysis due to the presence of fuzzy and highly overlapping cells. Deep learning-based diagnostic technology has proven to be effective in segmenting complex medical images. We present a two-stage framework based on Mask RCNN to automatically segment overlapping cells. In stage one, candidate cytoplasm bounding boxes are proposed. In stage two, pixel-to-pixel alignment is used to refine the boundary and category classification is also presented. The performance of the proposed method is evaluated on publicly available datasets from ISBI 2014 and 2015. The experimental results demonstrate that our method outperforms other state-of-the-art approaches with DSC 0.92 and FPRp 0.0008 at the DSC threshold of 0.8. Those results indicate that our Mask RCNN-based segmentation method could be effective in cytological analysis.

Detection of Trees on Street-View Images Using a Convolutional Neural Network

2021 ◽  
pp. 2150042
Author(s):  
Danilo Samuel Jodas ◽  
Takashi Yojo ◽  
Sergio Brazolin ◽  
Giuliana Del Nero Velasco ◽  
João Paulo Papa
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Efficient Solution ◽  
Urban Forest ◽  
Cost Effective ◽  
Ground Level ◽  
Forest Monitoring ◽  
Urban Landscapes ◽  
Street View ◽  
Bounding Boxes

Real-time detection of possible deforestation of urban landscapes is an essential task for many urban forest monitoring services. Computational methods emerge as a rapid and efficient solution to evaluate bird’s-eye-view images taken by satellites, drones, or even street-view photos captured at the ground level of the urban scenery. Identifying unhealthy trees requires detecting the tree itself and its constituent parts to evaluate certain aspects that may indicate unhealthiness, being street-level images a cost-effective and feasible resource to support the fieldwork survey. This paper proposes detecting trees and their specific parts on street-view images through a Convolutional Neural Network model based on the well-known You Only Look Once network with a MobileNet as the backbone for feature extraction. Essentially, from a photo taken from the ground, the proposed method identifies trees, isolates them through their bounding boxes, identifies the crown and stem, and then estimates the height of the trees by using a specific handheld object as a reference in the images. Experiment results demonstrate the effectiveness of the proposed method.

scholarly journals Convolutional Neural Network Analysis of Social Novelty Preference using DeepLabCut

2019 ◽  
Author(s):  
Nicholas B. Worley ◽  
Anthony Djerdjaj ◽  
John P. Christianson
Keyword(s):  
Neural Network ◽  
Social Interaction ◽  
Social Behavior ◽  
Convolutional Neural Network ◽  
Strongly Correlated ◽  
Novelty Preference ◽  
Accurate Identification ◽  
Laboratory Rodents ◽  
Human Effort ◽  
Social Decision Making

AbstractThe description and quantification of social behavior in laboratory rodents is central to basic and translational research. Conventional ethological approaches to social behavior are fraught with challenges including bias, significant human effort and temporal accuracy. Here we show proof of principle that machine learning can be applied to laboratory tests of social decision making. Rats underwent social novelty preference tests which were scored both by hand and again by a convolutional neural network generated in the DeepLabCut computer vision package of Mathis and colleagues. The CNN generated temporally (30Hz) and locally (<5pixels) accurate identification of rat nose, eye and ear positions which were then used to compute social interaction and topography heat maps. In sum, hand- and computer-scoring were strongly correlated, and each identified significant preferences to interact with novel conspecifics which sets the stage for applying DeepLabCut analysis to other types of social interaction in the future.

scholarly journals Deep Learning-Based Landmark Detection for Mobile Robot Outdoor Localization

Machines ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 25 ◽  
Author(s):  
Sivapong Nilwong ◽  
Delowar Hossain ◽  
Shin-ichiro Kaneko ◽  
Genci Capi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Mobile Robot ◽  
Convolutional Neural Network ◽  
Compass Orientation ◽  
Landmark Detection ◽  
Positioning Systems ◽  
Bounding Boxes ◽  
Gps Accuracy ◽  
Outdoor Localization

Outdoor mobile robot applications generally implement Global Positioning Systems (GPS) for localization tasks. However, GPS accuracy in outdoor localization has less accuracy in different environmental conditions. This paper presents two outdoor localization methods based on deep learning and landmark detection. The first localization method is based on the Faster Regional-Convolutional Neural Network (Faster R-CNN) landmark detection in the captured image. Then, a feedforward neural network (FFNN) is trained to determine robot location coordinates and compass orientation from detected landmarks. The second localization employs a single convolutional neural network (CNN) to determine location and compass orientation from the whole image. The dataset consists of images, geolocation data and labeled bounding boxes to train and test two proposed localization methods. Results are illustrated with absolute errors from the comparisons between localization results and reference geolocation data in the dataset. The experimental results pointed both presented localization methods to be promising alternatives to GPS for outdoor localization.

scholarly journals A Movie Recommendation System Based on A Convolutional Neural Network

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1455
Author(s):  
Jyoti Kumari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Cosine Similarity ◽  
The Other ◽  
Human Effort ◽  
Movie Recommendation ◽  
Content Based Filtering

Abstract: Due to its vast applications in several sectors, the recommender system has gotten a lot of interest and has been investigated by academics in recent years. The ability to comprehend and apply the context of recommendation requests is critical to the success of any current recommender system. Nowadays, the suggestion system makes it simple to locate the items we require. Movie recommendation systems are intended to assist movie fans by advising which movie to see without needing users to go through the time-consuming and complicated method of selecting a film from a large number of thousands or millions of options. The goal of this research is to reduce human effort by recommending movies based on the user's preferences. This paper introduces a method for a movie recommendation system based on a convolutional neural network with individual features layers of users and movies performed by analyzing user activity and proposing higher-rated films to them. The proposed CNN approach on the MovieLens-1m dataset outperforms the other conventional approaches and gives accurate recommendation results. Keywords: Recommender system, convolutional neural network, movielens-1m, cosine similarity, Collaborative filtering, content-based filtering.

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