Path detection has emerged as a complex challenge that has garnered significant attention within the computer vision community for a considerable span. The foundational problem of recognizing routes has evolved into a substantial impediment in the realm of computer vision. While a variety of Deep Learning methods have been employed for route identification, the focus has often centered on feature generation. Nonetheless, advanced Deep Learning techniques have exhibited proficiency in discerning these attributes. Yet, the complete implementation of these approaches to efficiently and accurately identify lanes is still pending. In this paper, we introduce an innovative perspective to address this issue. A distinctive approach to route preprocessing, coupled with a bird's-eye view, is presented. The primary aim involves the selection of the bird's-eye view area based on the processed image. Subsequently, an HSL (hue, saturation, and lightness) color transformation is applied to extract white features, along with a unique preliminary edge feature detection method. The lane is then identified using this new preprocessing strategy. It is noteworthy that numerous prior works have drawn upon the same standard datasets.