Tag Archive for: Complementary residual learning

Visual object detection is an artificial intelligence technique that locates specific objects from images, which is of great significance for practical applications. However, training general object detection models require many manually annotated images, bringing more labour and time cost. In order to improve the adaptability of the object detection model to the data environment changes, this paper proposes a self-learning object detection system based on high-reliability sample mining. We first train a SampleNet that can better mine reliable training samples from unlabeled data. We then use the combination of SampleNet and the basic object detection model to build a complementary residual training framework, continuously improving the sample mining ability and object detection tasks during the training process. The experimental results show that SampleNet can stably provide reliable pseudo samples for model training, and the complementary residual training framework improves the performance of basic object detection tasks.