  In recent years, deep learning based methods have achieved promising
performance in standard object detection. However, these methods lack
sufficient capabilities to handle underwater object detection due to these
challenges: (1) Objects in real applications are usually small and their images
are blurry, and (2) images in the underwater datasets and real applications
accompany heterogeneous noise. To address these two problems, we first propose
a novel neural network architecture, namely Sample-WeIghted hyPEr Network
(SWIPENet), for small object detection. SWIPENet consists of high resolution
and semantic rich Hyper Feature Maps which can significantly improve small
object detection accuracy. In addition, we propose a novel sample-weighted loss
function which can model sample weights for SWIPENet, which uses a novel sample
re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the
influence of noise on the proposed SWIPENet. Experiments on two underwater
robot picking contest datasets URPC2017 and URPC2018 show that the proposed
SWIPENet+IMA framework achieves better performance in detection accuracy
against several state-of-the-art object detection approaches.
