Vehicular automation is a rapidly advancing field within robotics. These autonomous machines have the potential to perform burdensome and dangerous tasks that historically have been executed by humans which has been a long-time goal for the industry. This thesis aims to develop a computer vision system to enable volume estimation and material classification of the material inside the bucket of an autonomous wheel loader. This information is crucial for autonomous wheel loaders to make decisions. The system is intended to be self-calibrating to ensure future adaptability to different bucket sizes. A Convolutional Neural Network (CNN) based edge detecting network referred to as Dense Extreme Inception Network for Edge Detection (DexiNed) is proposed to both remove redundant information and enhance desired information. By combining the depth perception from a stereo camera and the information extracted from the DexiNed a proposed solution to estimate the volume is presented. A Simple Linear Iterative Clustering (SLIC) approach is applied to extract the material to enable classification of the material. The estimated volume is compared to an annotated true baseline for validation of the system. The thesis presents the precision of the volume estimation and showcases the result of material extraction using three different segment sizes with the SLIC. Additionally, the thesis presents issues concerning material classification.