# Underwater object detection using Invert Multi-Class Adaboost with deep learning

arXiv: https://arxiv.org/abs/2005.11552 · HTML: https://mlsanity.com/paper/2005.11552/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2020.

## Built on by (most cited first, 9 of 9)

- [A Dataset And Benchmark Of Underwater Object Detection For Robot Picking](https://mlsanity.com/paper/2106.05681/) (2021)
- [AQUA20: A Benchmark Dataset for Underwater Species Classification under Challenging Conditions](https://mlsanity.com/paper/2506.17455/) (2025)
- [Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future](https://mlsanity.com/paper/2410.05577/) (2024)
- [Beneath the Surface: The Role of Underwater Image Enhancement in Object Detection](https://mlsanity.com/paper/2411.14626/) (2024)
- [An Underwater Image Semantic Segmentation Method Focusing on Boundaries and a Real Underwater Scene Semantic Segmentation Dataset](https://mlsanity.com/paper/2108.11727/) (2021)
- [Underwater target detection based on improved YOLOv7](https://mlsanity.com/paper/2302.06939/) (2023)
- [Class balanced underwater object detection dataset generated by class-wise style augmentation](https://mlsanity.com/paper/2101.07959/) (2021)
- [A Target Detection Algorithm in Traffic Scenes Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2312.15606/) (2023)
- [Underwater Object Detection Enhancement via Channel Stabilization](https://mlsanity.com/paper/2408.01293/) (2024)

## Abstract (arXiv)

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.