Cautionary Sign Analysis of Traffic Sign Data-Set Using Supervised Spiking Neuron Technique

Abstract

In this paper, 19 cautionary traffic signs were selected as a database and 3 types of conditions have been proposed. The conditions are 5 different time of image taken; hidden region and anticlockwise rotation are all the experiments design that will shows all the errors in producing the it’s mean value and the performance of traffic sign recognition. Initial hypothesis was made as the error will become larger as the interruption getting bigger. Based on the results of the five-different time of image taken, the error gives the best performance; less error when time is between 8am to 12am due to the brightness factors and the sign can be recognize clearly during noon session. The hidden region conditions show good performances of the detection and recognition of the system depend on the lesser coverage of the hidden region introduce on traffic sign because if the hidden region coverage is huge the database will get confuse and take a longer time to do the recognition process. Lastly, in anticlockwise rotation shows that 90o gave large value of error causing the system unable to recognize sign perfectly rather than 135o angle. To sum-up, detection and recognition process are not depending on higher number of angle but the process solely depending on their value of sample for each traffic signs. The error will give the impact towards traffic sign recognition and detection process. In conclusion, SNN can perform the detection and recognition process to all objects as in the future the system will become more stable with the right technique on spiking models and well-developed technology in this field.