Distorted dense analogs intelligent recognition in fisheye lenses by partially featured points calibrating and clustering.

Abstract

Dense analogs intelligent recognition (DAIR) has many potential applications in various fields as a new cross-disciplinary frontier of artificial intelligence and optical technology. However, with extensive application of fisheye lenses, inherent distortions in fisheye images have brought new challenges to DAIR. To solve this problem, we propose and experimentally demonstrate a partially featured points calibrating method that needs only correction of central points of the bounding boxes output by a convolutional neural network (CNN). The key to our method is a central-coordinate calibrating and clustering algorithm (CCCCA) based on a hemispheric double longitude projection model. Experimental results show that the CCCCA reduces the classification error rate by 6.05%, enhancing the classification accuracy of distorted DAIR up to 99.31%. Such classification accuracy is about 2.74% higher than that achieved by the mainstream online hard example mining algorithm, effectively modifying recognition errors induced by the CNN.