International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 06 | June 2022 www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
Object Detection for Autonomous Cars using AI/ML Kethang Kenneth Kath 1, Prashantha O 2, Amit Kamble 3, Prof. Anu C.S 4 1,2,3,4 Computer
Science and Engineering, BIET, Davanagere, India. ---------------------------------------------------------------------***------------------------------------------------------------------Abstract— The purpose of this project is to research the III. EXISTING SYSTEM ethics for the acquisition of object detection for driverless cars. The world of Computer Vision is continuously rising with a rise in interaction between human and machine. Selfdriving cars have deployed the utilization of GPS. Our paper proposes a result which will identify and rightly prognosticate objects round the driverless car. The aptitude to perceive and identify objects from camera perspective are the main aim of our paper. We have used MobileNet-SSD, an object discovery model that calculates the bounding boxes and categorizes an item from an inserted image. This paper plans to coach the reader to higher understand how autonomous cars scans its surroundings.
In the existing system, the model makes use of the Camera Sensor. Assume a sunny day suddenly turns into a rainy and foggy day. In such cases, the sensor finds it hard to detect the target object. That is where the computer vision comes into play. Computer Vision is a field of Artificial Intelligence that trains computers to understand the visual world by training the model.
IV. PROPOSED SYSTEM Accuracy plays an important role inprediction. Although many algorithms are available for this purpose, we will be using the SSD to classify the objects. We make use of different libraries to form a network and also use TensorFlow. Once the training is done our next objective will be to test the model for accuracy.
Keywords— object detection, image-processing, computer vision , object classification
I. INTRODUCTION
V. OBJECTIVES
Allowing computer to output digital information from photos or videos falls into the sector of computer vision (CV). Image Classification was considered as a recurrent drawback in computer vision and was flagged as unsolvable by many researchers that was until Lawrence Larry Roberts extracted 3D features from 2D perspective view of blocks. The potential to spot and recognize objects either in single or quite one image frame can gain extreme significance in various ways. While driving, the driver is unable to identify objects properly due to a lack of attention, light reflections, unknown objects, etc., which can result in car crashes. The idea of self-driving vehicles has been evolving with the advancement in ways associated with the task of relating and rooting features from the objects.
II.
EASE OF USE
A.
Computer Vision(CV)
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Impact Factor value: 7.529
To detect, classify, and track the object for autonomous car.
To Process the images frames to detects the object using a Lidar.
To Deploy models in the cloud and determines the objects with good accuracy value.
VI. LITERATURE SURVEY SUMMARY [1] Wang Junqiang, Li Jiansheng, Zhou Xuewen, and Zhang Xu proposed an improved version of SSD which aims at solving the trials and errors of a slow detection speed. [2] J Jeong, P. Hyojin , K. Nojun proposes an object detection method that improve the accuracy of the standard SSD object detection algorithms in terms of precision and rapidness. The proposed network is suitable for sharing weights within the classifier network. This improves generalization performance and speeds up training. The suggested network has a state-of-the-art mean average precision, that is more superior to standard SSD, YOLO, Faster-RCNN and RFCN. It is also faster than Faster-RCNN and R-FCN
Computer Vision is a field of AI allowing computers to amass meaningful information in digital images that works the same as human eye. Computer vision trains machines to perform these tasks, but it’s job is to try and do it within a short period of time with cameras, data and algorithms instead of retina, optic nerve and a visible peridium. System trained for examining products or watching product assets can analyze thousands of products or process a moment, noticing all small defects and issues and also it can quickly surpass human capabilities.
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