Beschreibung:
<jats:title>Abstract</jats:title>
<jats:p>YOLOv3, a target detection algorithm based on deep learning, is widely applied in object recognition, especially in guiding the blind. The existing products of assisting the blind based on YOLOv3 can already achieve high-precision, high-real-time object recognition. But YOLOv3 also has many limitations, such as the inability to measure distances or it’s hard to recognize objects correctly in fog or haze. For these deficiencies, this paper proposes a road barrier monitoring method based on improved YOLOv3, using an image downsampling algorithm based on the dark channel to defog the image, and then with the binocular distance measurement algorithm to calculate the obstacles from the distance of the camera according to the width and height of the obstacles. The experimental results show that the improved product retains the advantages of high accuracy and fast recognition speed of YOLOv3. At the same time, it also owns the new functions of obstacle ranging and bad weather identification. The improved algorithm can meet the requirements of portability, real-time, and practicality of guide products.</jats:p>