Abstract
An obstacle detection method based on VM (VIDAR and machine learning joint detection model) is proposed to improve the monocular vision system's identification accuracy. When VIDAR (Vision-IMU-based detection and range method) detects unknown obstacles in a reflective environment, the reflections of the obstacles are identified as obstacles, reducing the accuracy of obstacle identification. We proposed an obstacle detection method called improved VM to avoid this situation. The experimental results demonstrated that the improved VM could identify and eliminate unknown obstacles. Compared with more advanced detection methods, the improved VM obstacle detection method is more accurate. It can detect unknown obstacles in reflection, reflective road environments.
Highlights
Due to the low cost, high detection accuracy, and speed of vision-based obstacle identification equipment, it has become more suitable for various vehicles [4, 5]. e vision-based sensor used in this study is a camera
To avoid the situation in which VIDAR detects the reflection as an obstacle when used in a reflective environment (Figure 2) and improve detection accuracy, a VIDAR-based pseudo-obstacle detection method has been proposed. is method’s identification procedure is as follows. e rectangle of the obstacle was determined. e width of the obstacle rectangle was calculated using the transformation relationship between pixel coordinates and world coordinates, and the height of Journal of Robotics the obstacle rectangle was calculated using the transformation relationship between pixel coordinates and world coordinates. e true obstacle is determined by the fact that the actual height of the obstacle rectangle remains constant throughout the ego-vehicle movement
To accelerate the detection speed of improved VIDAR, we combined it with machine learning to identify known obstacles, which we refer to as improved VM. e improved VM obstacle detection method can quickly and accurately detect obstacles on reflection roads. e enhanced VM obstacle detection procedure is as follows: first, machine learning is used to identify known obstacles; second, the identified obstacles are removed from the background area; and pseudoobstacles are eliminated through the use of enhanced VIDAR
Summary
Obstacle detection has become a major concern in the field of driver assistance systems due to the complexity of the outdoor environment. While lidar and millimeter-wave radar are highly accurate at detecting obstacles, their high cost limits their use in low-end vehicles [1–3]. Due to the low cost, high detection accuracy, and speed of vision-based obstacle identification equipment, it has become more suitable for various vehicles [4, 5]. Machine learning is used to identify known obstacles in the proposed method, while VIDAR is used to detect unknown obstacles. To avoid the situation in which VIDAR detects the reflection as an obstacle when used in a reflective environment (Figure 2) and improve detection accuracy, a VIDAR-based pseudo-obstacle detection method (called improved VIDAR) has been proposed. To accelerate the detection speed of improved VIDAR, we combined it with machine learning (this article uses the faster RCNN algorithm) to identify known obstacles, which we refer to as improved VM. To accelerate the detection speed of improved VIDAR, we combined it with machine learning (this article uses the faster RCNN algorithm) to identify known obstacles, which we refer to as improved VM. e improved VM obstacle detection method can quickly and accurately detect obstacles on reflection roads. e enhanced VM obstacle detection procedure is as follows: first, machine learning is used to identify known obstacles; second, the identified obstacles are removed from the background area; and pseudoobstacles are eliminated through the use of enhanced VIDAR
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