BANet: A Balanced Atrous Net Improved From SSD for Autonomous Driving in Smart Transportation

Object detection for autonomous driving in smart transportation systems requires comprehensive consideration of accuracy, speed and sensitivity for detecting multi-objects. The one-stage algorithm, Single Shot MultiBox Detector (SSD), can basically satisfy the above requirements. However, there are still rooms for improvement of the overall precision due to its dissatisfactory detection rate of small objects, which are abundant in autonomous driving scenarios. In order to solve the inherent defect problem of one-stage algorithm in processing the extreme foreground-background class imbalance encountered during training of dense detectors, we propose a Balanced Atrous Net (BANet) to significantly improve the performance of the SSD for autonomous driving. The BANet combines the atrous convolution and the feature fusion to improve the network structure of the SSD, thereby expanding receptive field and enriching semantic information in shallow layers. Meanwhile, a new loss that alleviates the class imbalance is designed to replace the standard cross entropy loss in the original algorithm. The experimental results show that the BANet outperforms the original SSD in mAP on KITTI with a relatively high speed retained.