Fast and accurate obstacle detection based on stereo vision and deep learning

Abstract. The ability to detect and avoid obstacles is essential for ensuring the safe and efficient navigation of mobile robots. With the increasing demand for intelligent autonomous systems, the need for fast and accurate obstacle detection has become a critical area of research. Existing methods for obstacle detection can be broadly classified into three categories: sensor-based, image-based, and hybrid approaches. Among these, vision-based techniques have gained significant attention due to their effectiveness and versatility. These methods can be further divided into monocular and stereo approaches, each offering distinct advantages. In recent years, stereo vision-based methods have emerged as a promising solution for obstacle detection, as they enable the precise estimation of depth and distance to objects, providing valuable information for real-time navigation. However, despite their accuracy, stereo methods are often criticized for their high computational complexity and processing inefficiency, which can limit their practicality in high-speed robotic applications. This study presents a novel hybrid approach that integrates the advantages of both stereo and monocular methods. By leveraging monocular techniques for object detection and utilizing stereo vision for precise depth estimation, our method enhances efficiency while maintaining high accuracy. Instead of computing depth information for individual pixels, the proposed method operates at the object level, calculating the distance for detected objects rather than processing each pixel separately. In addition, comparative analysis with the method developed by Lamini, Fathi et al. 2024 demonstrates that the proposed approach yields more stable and accurate results, further highlighting its effectiveness and reliability in obstacle detection.