The daily traffic volume is increasing, and there is insufficient infrastructure to manage it effectively. This study aims to improve traffic identification outcomes by conducting a comparative analysis between the Sobel and Canny edge detection techniques using image processing. The analysis is performed across various parameters to determine the superior approach and subsequently develop an advanced technique for data abstraction in the context of the traffic system. Only an edge map with accurate edges can provide useful information. Edge detection is a highly challenging process, and our goal is to achieve precise edge location of the vehicle, which further aids in detecting the vehicle. The primary objective of our proposed research is to enhance the accuracy and speed of vehicle detection by developing an efficient algorithm. We aim to exceed the performance of existing algorithms in the field and provide a more precise and reliable solution for vehicle detection. The proposed algorithm will assist in managing traffic congestion by utilizing edge gradient direction and magnitude images. Our study aims to contribute to the field of traffic management by providing an effective solution for detecting vehicles.