A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles

Unmanned ground vehicles (UGVs) are an important research application of artificial intelligence. In particular, the deep learning-based object detection method is widely used in UGV-based environmental perception. Good experimental results are achieved by the deep learning-based object detection method Faster region-based convolutional neural network (Faster R-CNN). However, the exploration space of the region proposal network (RPN) is restricted by its expression. In our paper, a boosted RPN (BRPN) with three improvements is developed to solve this problem. First, a novel enhanced pooling network is designed in this paper. Therefore, the BRPN can adapt to objects with different shapes. Second, the expression of BRPN loss function is improved to learn the negative samples. Furthermore, the grey wolf optimizer (GWO) is used to optimize the parameters of the improved BRPN loss function. Thereafter, the performance of the BRPN loss function is promoted. Third, a novel GA-SVM classifier is applied to strengthen the classification capacity. The PASCAL VOC 2007, VOC 2012 and KITTI datasets are used to test the BRPN. Consequently, excellent experimental results are obtained by our deep learning-based object detection method.