Improved Convolutional Neutral Network Based Model for Small Visual Object Detection in Autonomous Driving

Abstract

As the killer application of artificial intelligence, autonomous driving is making fundamental transformations to the transportation industry. Computer vision based on deep learning is among the enabling technologies. However, small objects around vehicles are difficult to detect because of poor visual features within small objects as well as insufficient valid samples of small objections. In this paper, we propose an end-to-end detector model based on convolutional neutral network (CNN) to enhance visual features of small traffic signs in real scenarios. With those enhanced features, we manage to obtain an efficient inference model after training. We further make preliminary comparison with Fast R-CNN and Faster R-CNN models. Experimental results indicate that our model outperforms the others by more than 10% improvement in terms of accuracy and recall.