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 semanticrich 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.
|Title of host publication||2020 International Joint Conference on Neural Networks, IJCNN 2020: Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - Jul 2020|
|Event||2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom|
Duration: 19 Jul 2020 → 24 Jul 2020
|Name||Proceedings of the International Joint Conference on Neural Networks|
|Name||International Joint Conference on Neural Networks (IJCNN): Proceedings|
|Conference||2020 International Joint Conference on Neural Networks, IJCNN 2020|
|Period||19/07/2020 → 24/07/2020|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT Thanks for National Natural Science Foundation of China and Dalian Municipal People’s Government providing the underwater object detection datasets for research purposes. This project of underwater object detection is supported by China Scholarship Council.
© 2020 IEEE.
Copyright 2020 Elsevier B.V., All rights reserved.
- Multi-Class Adaboost
- noisy data
- sample re-weighting
- underwater object detection
ASJC Scopus subject areas
- Artificial Intelligence