KPCC: hard example mining of dense analogs in fisheye lens

In recent years, there has been a substantial rise in the utilization of fisheye lenses, which offer a wide field-of-view. However, the distortion inherent in these lenses presents a major challenge for intelligent recognition of dense analogs (IRDA) in applications based on convolutional neural network (CNN). To enhance the accuracy of IRDA, we introduce a novel algorithm called Key Point Calibrating and Clustering (KPCC), which is based on an equidistant projection model. Our method can fully mine hard examples and effectively correct their misclassifications predicted by the CNN, thereby significantly improving the accuracy of IRDA.