Class-quantity and class-difficulty based methods for long-tailed road marking detection

Existing methods for object detection in road marking images ignored an important challenge-imbalanced class distribution in road marking images-which lead to poor performance on tail classes. Existing approaches to this issue focus mostly on data quantity. However, throughout the training process, the quantity and difficulty of each class are two related and equally important problems. To this end, we propose a framework, tripling sampler, and head detection network (TSHNet), which consists of class-preference samplers (CPS) and trilateral box heads (TBH). The CPS is composed of two complementary factors: the quantity factor and the difficulty factor. TBH is designed to handle tail&hard classes, common classes, and head&easy classes in a triple-path manner. We evaluate our approach on CeyMo and road marking datasets and achieve excellent performance when combined with PolyLoss. Our results demonstrate that TSHNet significantly outperforms base detectors and generic approaches for long-tail road marking problems.