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A Smart Vehicular Forward Collision Alert System using Neural Network

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Felix Larbi Aryeh, Emmanuel Kweku Eshun, Abdul-Harisu Inusah

Felix Larbi Aryeh, Emmanuel Kweku Eshun and Abdul-Harisu Inusah. A Smart Vehicular Forward Collision Alert System using Neural Network. International Journal of Computer Applications 183(21):8-17, August 2021. BibTeX

	author = {Felix Larbi Aryeh and Emmanuel Kweku Eshun and Abdul-Harisu Inusah},
	title = {A Smart Vehicular Forward Collision Alert System using Neural Network},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2021},
	volume = {183},
	number = {21},
	month = {Aug},
	year = {2021},
	issn = {0975-8887},
	pages = {8-17},
	numpages = {10},
	url = {},
	doi = {10.5120/ijca2021921575},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Road accidents over the years have led to an increased mortality rate in Ghana. Most accidents are caused by stress, tiredness, and loss of concentration of drivers while driving. This paper sought to design and build an automatic detection forward collision alert system for already existing vehicles in Ghana. Specifically, a deep learning model was built using a convolutional neural network for detection of stationary/moving objects (person, cars, bicycle, truck, bus and others). The object's relative speed to that of the subject vehicle is used to determine the probability of a collision. A buzzing alarm sound occurs when the possibility of collision is higher. The proposed system is an economical technique of alerting drivers of any impending danger on our roads.


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You Only Look Once (YOLO), Visual Geometry Group (VGG), Single Shot Multibox Detector (SSD), Advanced Driver Assistance System (ADAS).