DEEP LEARNING-BASED OBSTACLE DETECTION AND DISTANCE ESTIMATION USING OBJECT BOUNDING BOX

In this paper, a system consisting of deep learning (DL)-based object detection followed by neural network based object distance estimation is considered. The accuracy of object distance estimation strongly depends on the size of the bounding box (BB) of the detected object extracted by the DL-based object detector. A method for improvement of the accuracy of object BB is proposed, which involves traditional computer vision-based edge segmentation of object BB image region. The proposed method is evaluated on the real-world images of railway scenes with obstacles on the rail tracks captured by thermal and RGB cameras. The evaluation results demonstrate the potential of traditional computer vision methods to complement state-of-the-art DL methods for accurate object detection and distance estimation.