SSD Based Target Detection Algorithm for the Autonomous Driving Vision System

Target detection algorithm takes a central role in autonomous driving vision system, participating in a wide rang of applications in the obstacle detection, vehicle detection, traffic state detection, and others. In recent years, accurate and efficient deep learning models have been proposed for real-time applications. However, the most accurate approaches tend to employ efficient feature fusion method and improved training strategy, such as spatial pyramid pooling. This practice limits real-time performance due to intense extra computational cost. In this paper, we avoid the traditional feature fusion method by proposing an efficient feature fusion method based on feature bi-directional fusion technique and introduce a training method with noise based on confidence loss sequence. We demonstrate the efficiency of the proposal on the PASCAL VOC2007 and KITTI sets, which include the common target detection task and typical autonomous driving target recognition task. The experimental results show that our approach significantly improve the accuracy for complex background samples and reduce the false detection rate of small targets by keeping real-time performance.