1 research outputs found
SWIPENET: Object detection in noisy underwater images
In recent years, deep learning based object detection methods have achieved
promising performance in controlled environments. However, these methods lack
sufficient capabilities to handle underwater object detection due to these
challenges: (1) images in the underwater datasets and real applications are
blurry whilst accompanying severe noise that confuses the detectors and (2)
objects in real applications are usually small. In this paper, we propose a
novel Sample-WeIghted hyPEr Network (SWIPENET), and a robust training paradigm
named Curriculum Multi-Class Adaboost (CMA), to address these two problems at
the same time. Firstly, the backbone of SWIPENET produces multiple high
resolution and semantic-rich Hyper Feature Maps, which significantly improve
small object detection. Secondly, a novel sample-weighted detection loss
function is designed for SWIPENET, which focuses on learning high weight
samples and ignore learning low weight samples. Moreover, inspired by the human
education process that drives the learning from easy to hard concepts, we here
propose the CMA training paradigm that first trains a clean detector which is
free from the influence of noisy data. Then, based on the clean detector,
multiple detectors focusing on learning diverse noisy data are trained and
incorporated into a unified deep ensemble of strong noise immunity. Experiments
on two underwater robot picking contest datasets (URPC2017 and URPC2018) show
that the proposed SWIPENET+CMA framework achieves better accuracy in object
detection against several state-of-the-art approaches.Comment: arXiv admin note: text overlap with arXiv:2005.1155