Abstract
Stress and sudden difficult situations have raised the risks of accidents down the roads. The drivers’ attention might be distracted out in seconds under unexpected circumstances, which could take place due to bad weather, vision problems, fatigue for long driving hours, damaged or broken Traffic light , and even children's noise inside the car. In this paper, I proposed to develop a special colourful Deep Back Propagation Neural Network to enhance drivers’ attention by observing different traffic light cases using a suggested smart binary matching machine system in Python. The smart machine system will analyse and identify the real Traffic light from art signs, broken or damaged ones; in addition to pedestrian signs based on a Database symbols for each case, which have taken the basic Traffic light and signs, and developed them to damaged cases or unreal one, before making the right decision by the learned network, then send an enhanced feedback signal to the driver. The algorithm consisted of accurate image processing steps, with two long stages of full contents features extraction vectors to be handled by Red-Yellow-Green Shallow and Deep Back Propagation Neural Networks (SBPNN) and (DBPNN) for each complex case. As a result, the algorithm rated a high accuracy of 100%, which is the most important factor to maintain safety, recoding the true label output as 1-value, with a predicated tested ouput 1.0-value. The suggested system does not replace the driver's one decision, yet it is an enhancing backup classification and recognition system before things move out of control. The feedback signal calculated based on reducing costs for 2500 iterations with The leas minimum value 000012,and can be developed as a voice signal warning Message, to increase the awareness of the drivers, besides the warning text on the screen.
