Cost-effective annotation of fisheye images for object detection

Nowadays, fisheye image has become commonly used in the 3D reality capturing field. Although AI integration for image recognition has become mature with normal images, providing available annotated dataset and pre-trained models, its application for fisheye images is rarely seen. While the object detection models have generalization ability, dealing with barrel distortion requires specific data for fine-tuning. This paper seeks to acquire prior knowledge from normal image and transfer it to the application that deal with fisheye images. This research is devoted to test the annotation shape that could possibly improve the accuracy when representing the shape of objects. It also seeks a way to prove that the annotation can be converted to fisheye images, resulted into a pre-process, which will facilitate the data preparation process. The tests involve annotations with standard box and quadrilateral polygon, the later turned out to be preserving most of the wanted image content after the conversion. The test result shows that the model trained on converted annotations using quadrilateral polygons, compared to detection model trained on non-converted ones, improves the mean average precision by 8%.