Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss

Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design. In this paper, we propose a novel regression loss based on Gaussian Wasserstein distance as a fundamental approach to solve the problem. Specifically, the rotated bounding box is converted to a 2-D Gaussian distribution, which enables to approximate the indifferentiable rotational IoU induced loss by the Gaussian Wasserstein distance (GWD) which can be learned efficiently by gradient back-propagation. GWD can still be informative for learning even there is no overlapping between two rotating bounding boxes which is often the case for small object detection. Thanks to its three unique properties, GWD can also elegantly solve the boundary discontinuity and square-like problem regardless how the bounding box is defined. Experiments on five datasets using different detectors show the effectiveness of our approach. Codes are available at https://github.com/yangxue0827/RotationDetection.Comment: 15 pages, 6 figures, 9 tables, accepted by ICML21, codes are available at https://github.com/yangxue0827/RotationDetectio

Surgical enucleation of unilateral mandibular radicular cysts in an 11‐year‐old Thoroughbred mare

An 11‐year‐old Thoroughbred broodmare was diagnosed with extensive radicular cysts that affected the left horizontal mandibular ramus. A left mandibular swelling was present clinically, and mandibular lesions appearing cystic in nature were identified with successive radiographic and ultrasonographic examinations. Surgical enucleation of the cysts was performed under a standing sedation protocol and the excised cystic structures submitted for histopathological analysis. Histological examination of the cystic structures confirmed a diagnosis of multiple radicular cysts.https://beva-onlinelibrary-wiley-com/journal/204232922020-06-01hj2020Companion Animal Clinical Studie