scholarly journals Vehicle Speed Detection and Safety Distance Estimation Using Aerial Images of Brazilian Highways

2020 ◽  
Author(s):  
Mateus Eloi da Silva Bastos ◽  
Vitor Yeso Fidelis Freitas ◽  
Richardson Santiago Teles De Menezes ◽  
Helton Maia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Distance Estimation ◽  
Mean Average Precision ◽  
Aerial Images ◽  
Vehicle Speed ◽  
Safe Distance ◽  
Training Set ◽  
Average Precision ◽  
Safety Distance

In this study, the computational development conducted was based on Convolutional Neural Networks (CNNs), and the You Only Look Once (YOLO) algorithm to detect vehicles from aerial images and calculate the safe distance between them. We analyzed a dataset composed of 896 images, recorded in videos by a DJI Spark Drone. The training set used 60% of the images, 20% for validation, and 20% for the tests. Tests were performed to detect vehicles in different configurations, and the best result was achieved using the YOLO Full-608, with a mean Average Precision(mAP) of 95.6%. The accuracy of the results encourages the development of systems capable of estimating the safe distance between vehicles in motion, allowing mainly to minimize the risk of accidents.

scholarly journals Mice Tracking Using The YOLO Algorithm

2020 ◽  
Author(s):  
Richardson Santiago Teles De Menezes ◽  
John Victor Alves Luiz ◽  
Aron Miranda Henrique-Alves ◽  
Rossana Moreno Santa Cruz ◽  
Helton Maia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Behavioral Neuroscience ◽  
High Accuracy ◽  
Mean Average Precision ◽  
Computational Tool ◽  
Training Set ◽  
Average Precision ◽  
The Mean

The computational tool developed in this study is based on convolutional neural networks and the You Only Look Once (YOLO) algorithm for detecting and tracking mice in videos recorded during behavioral neuroscience experiments. We analyzed a set of data composed of 13622 images, made up of behavioral videos of three important researches in this area. The training set used 50% of the images, 25% for validation, and 25% for the tests. The results show that the mean Average Precision (mAP) reached by the developed system was 90.79% and 90.75% for the Full and Tiny versions of YOLO, respectively. Considering the high accuracy of the results, the developed work allows the experimentalists to perform mice tracking in a reliable and non-evasive way.

scholarly journals Estimation of Safety Distance Between Vehicles on Highways Using YOLOv4 from Aerial Images

2021 ◽  
Author(s):  
Vitor Yeso Fidelis Freitas ◽  
Richardson Santiago Teles Menezes ◽  
Francisco Vidal ◽  
Helton Maia
Keyword(s):  
Network Architecture ◽  
Traffic Accidents ◽  
Aerial Images ◽  
Vehicle Speed ◽  
Safe Distance ◽  
Average Precision ◽  
Moving Vehicle ◽  
Safety Distance ◽  
Moving Vehicles ◽  
Operational Errors

Traffic accidents are among the most worrying problems in modern life, often caused by human operational errors such as inattention, distraction, and misbehavior. Vehicle speed detection and safety distance measurement can help reduce these accidents. In this study, the computational development conducted was based on Convolutional Neural Networks (CNNs) and the You Only Look Once (YOLO) algorithm to detect vehicles from aerial images and calculate the safe distance and the vehicle’s speed on Brazilian highways. The investigation was conducted to model the YOLO algorithm for detecting vehicles in different network architecture configurations. The best results were obtained with the YOLO Full-608, reaching a mean Average Precision (mAP) of 97.44%. Additional computer vision approaches have been developed to calculate the speed of the moving vehicle and the safe distance between them. Therefore, the developed system allows that, based on detecting the safe distance between moving vehicles on the highways, accidents are predicted and possibly avoided.

scholarly journals Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN

2019 ◽  
Vol 8 (2S3) ◽  
pp. 824-829 ◽  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Problem Solving ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Mean Average Precision ◽  
Average Precision ◽  
Trade Off ◽  
Detection Techniques

Object detection has boomed in areas like image processing in accordance with the unparalleled development of CNN (Convolutional Neural Networks) over the last decade. The CNN family which includes R-CNN has advanced to much faster versions like Fast-RCNN which have mean average precision(Map) of up to 76.4 but their frames per second(fps) still remain between 5 to 18 and that is comparatively moderate to problem-solving time. Therefore, there is an urgent need to increase speed in the advancements of object detection. In accordance with the broad initiation of CNN and its features, this paper discusses YOLO (You only look once), a strong representative of CNN which comes up with an entirely different method of interpreting the task of detecting the objects. YOLO has attained fast speeds with fps of 155 and map of about 78.6, thereby surpassing the performances of other CNN versions appreciably. Furthermore, in comparison with the latest advancements, YOLOv2 attains an outstanding trade-off between accuracy and speed and also as a detector possessing powerful generalization capabilities of representing an entire image

Semantic Segmentation of Aerial Images With Shuffling Convolutional Neural Networks

2018 ◽  
Vol 15 (2) ◽  
pp. 173-177 ◽  
Author(s):  
Kaiqiang Chen ◽  
Kun Fu ◽  
Menglong Yan ◽  
Xin Gao ◽  
Xian Sun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Aerial Images

scholarly journals Multiple-Oriented and Small Object Detection with Convolutional Neural Networks for Aerial Image

2019 ◽  
Vol 11 (18) ◽  
pp. 2176 ◽  
Author(s):  
Chen ◽  
Zhong ◽  
Tan
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Aerial Images ◽  
Superior Performance ◽  
Aerial Image ◽  
Detection Accuracy ◽  
Small Object ◽  
Data Set ◽  
Orientation Information

Detecting objects in aerial images is a challenging task due to multiple orientations and relatively small size of the objects. Although many traditional detection models have demonstrated an acceptable performance by using the imagery pyramid and multiple templates in a sliding-window manner, such techniques are inefficient and costly. Recently, convolutional neural networks (CNNs) have successfully been used for object detection, and they have demonstrated considerably superior performance than that of traditional detection methods; however, this success has not been expanded to aerial images. To overcome such problems, we propose a detection model based on two CNNs. One of the CNNs is designed to propose many object-like regions that are generated from the feature maps of multi scales and hierarchies with the orientation information. Based on such a design, the positioning of small size objects becomes more accurate, and the generated regions with orientation information are more suitable for the objects arranged with arbitrary orientations. Furthermore, another CNN is designed for object recognition; it first extracts the features of each generated region and subsequently makes the final decisions. The results of the extensive experiments performed on the vehicle detection in aerial imagery (VEDAI) and overhead imagery research data set (OIRDS) datasets indicate that the proposed model performs well in terms of not only the detection accuracy but also the detection speed.

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