Object recognition is essential for autonomous cars, and the amalgamation of camera and light detection and ranging (LiDAR) sensor data has emerged as a pivotal method for accurate three-dimensional (3D) object recognition. Contemporary algorithms face challenges with fragmented data, high processing costs, insufficient resolution, and limited dynamic information. This study presents a novel approach utilising 3D U-Net deep learning for precise 3D object detection and localisation by integrating camera and LiDAR data. The process involves obtaining and preprocessing camera and LiDAR data, utilising a geometric 3D frustum method to extract 3D information from LiDAR based on 2D camera bounding boxes, and training a You Only Look Once version 4 (YOLO v4) network to recognise these boundaries in camera images. The detected images are combined with LiDAR data, and a deep U-Net network is utilised to define 3D bounding boxes. Performance is assessed at various noise levels (0 %, 1 %, 2 %, 5 %, and 10 %) in the composite images. This method leverages the benefits of both sensors to better object recognition across diverse shapes and sizes, even in challenging situations, signifying a significant progression towards safer and more reliable autonomous vehicles with improved situational awareness in intricate urban environments.