Due to the lack of vehicle-to-infrastructure (V2I) communication in the existing transportation systems, traffic light detection and recognition is essential for advanced driver assistant systems (ADAS) and road infrastructure surveys. Additionally, autonomous vehicles have the potential to change urban transportation by making it safe, economical, sustainable, congestion-free, and transportable in other ways. Because of their limitations, traditional traffic light detection and recognition algorithms are not able to recognize traffic lights as effectively as deep learning-based techniques, which take a lot of time and effort to develop. The main aim of this research is to propose a traffic light detection and recognition model based on a transfer learning-based model that uses the Inception-V3 network model to significantly reduce the amount of training data and computing costs. The proposed model was trained and tested in the laboratory for the intelligent and safe automobiles (LISA) traffic light dataset, which has been augmented by several pre-processing methods. Then, using different convolution and pooling techniques, the retrieved layer-wise features were compared and analyzed. Lastly, great reliability and repeatability are seen based on statistical analysis when the transfer learning-based model is frequently retrained utilizing precise tuning parameters. The results demonstrate that a transfer learning-based model is capable of high-level recognition performance in the proposed model, with an accuracy rate of 98.6%.