Hybrid Vision Perception for Low-Cost Autonomous Vehicle Navigation

Abstract

Highlights What are the main findings? The hybrid perception system combines ArUco localization, Canny edge detection, and YOLOv4 object detection to generate forward, turning, and stopping commands on a commercially available four-wheel electric vehicle platform fitted with the prototype perception and control system. Among the evaluated edge detectors, Canny achieved the highest performance, with a precision of 0.99 and a recall of 0.98, supporting its use within the navigation pipeline. What are the implications of the main findings? A low-cost monocular vision architecture can provide useful localization, boundary detection, and obstacle awareness without requiring a large LiDAR-, RADAR-, or multi-camera sensor suite. The successful laboratory and controlled outdoor deployments demonstrate the feasibility of integrating lightweight classical vision and deep object detection for resource-constrained autonomous navigation platforms. Abstract High-performance perception systems for autonomous vehicles (AVs) commonly use LiDAR, RADAR, and multiple cameras. These sensors provide useful information, but their cost and computational requirements can be difficult to justify for small prototypes and low-cost platforms. This work therefore uses a monocular camera and combines three vision methods: Canny edge detection for boundary information, ArUco markers for localization, and YOLOv4 for object detection. The outputs are used to generate navigation commands on a commercially available four-wheel electric vehicle platform equipped with the prototype control system. The experiments compare the individual image-processing methods and evaluate the complete system under different visual conditions. The results indicate that the combined approach can provide useful localization and obstacle detection without requiring a large sensor suite.