Cave Targets Recognition on Meta-Convolutional Networks and Lifelong Learning

Cave target is the high-value recognizing target. According to the difficulty about cave target data collection, the high data similarity, the limitation of the artificial feature, and the deep neural networks needs massive data, a method of combining meta-convolutional network and deep convolutional networks named meta-convolutional networks(MCNN), and combining lifelong learning was proposed (MCNN-LLS). Firstly, a meta-convolutional network was established by combining deep convolutional network and meta-learning. This network can use old knowledge to guide the training process, and can use the small sample to train an ideal cave detection model. Then combining lifelong learning and establishing the lifelong learning system (LLS), designing the expert review model to identify the recognition results by the cave detection model, and introducing potential tasks, model asynchronously update to reach the effect of model sustainable updating. Experiments show that this method only needs small sample, has high accuracy of recognizing cave target, and the recognition effect can gradually increase with the accumulation of new data.