Abstract
We propose a system which will detect objects onour roads, estimate the distance of these object from the cameraand alert the driver if this distance is equal or less than thethreshold value(02meters),and assist the driver and alert him assoon as possible in order for him to take appropriate actions assoon as possible which can avoid any collision or significantlyreduce it. We plan to use state of the arts object detection modelslike YOLO to identify the target object classes and use depthmaps from monocular camera to be give an accurate estimate ofthe distance of the detected object from the camera. one majorrequirement of this system is the real-time behaviour and a highaccuracy for the detected and estimated distance, A secondrequirement is to make the system cheap and easy useablecomparatively to the other existing methods. That is why wedecided to use monocular camera images and depth maps whichmakes the solution cheap and innovative. This project(prototype) provide room for bigger and more complete projectwhich will contribute to the creation of tool which can save livesand improve security on our roads
Highlights
We will focus on how deep learning and computer vision technique can be used to estimate the distance of the detected object (s) and raise an alarm if this distance is within the danger range, this can help boost the driver to take action in order to avoid the crash by reducing the speed significantly
This paper is mainly motivated by a research report released in 2018 by the World Health Organization (WHO) which showed how alarming was the number of deaths on our roads each year[8].This same report says that if nothing is done road accident will be the 5th cause of death among the youths by 2030. there is no barrier preventing us to take advantage of these to propose a solution
Computer vision researches are widely using deep learning, in general object recognition using deep learning have two approaches the first one which is known as a two stages approach (R-CNN) comparing to previously published sliding window-based techniques provides a remarkable improvement [9], an unattended algorithm for feature generating extraction by CNN's separately is used in R-CNN known as selective search . to estimate the classes of objects SVM classifier is applied by R-CNN in the last step, and to fine-tune the positions and sizes of detection boxes a linear regression is applied to get a better performance
Summary
In the field of developing autonomous driving cars computer vision has become a key component, due the advances in visual environment perception like object classification, detection, segmentation and distance estimation Techniques .Researchers mainly focus on object classification, detection and segmentation [1, 2, 3], beside a lot of efforts on improving the accuracy of visual perception crucial information for cars to avoid collisions, adjust its speed for safety driving can be provided through distances estimating between camera sensors [25,26,27] and recognized objects (e.g. cyclists , pedestrians ,cars) and by recognizing the objects on the way. We will focus on how deep learning and computer vision technique can be used to estimate the distance of the detected object (s) and raise an alarm if this distance is within the danger range, this can help boost the driver to take action in order to avoid the crash by reducing the speed significantly
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