论文标题
使用倒置多级adaboost的水下对象检测，深度学习
Underwater object detection using Invert Multi-Class Adaboost with deep learning
论文作者
论文摘要
近年来，基于深度学习的方法在标准对象检测中实现了有希望的性能。但是，由于这些挑战，这些方法缺乏足够的能力来处理水下对象检测：（1）实际应用中的对象通常很小，图像很模糊，并且（2）水下数据集中的图像和真实的应用程序伴随着异质噪声。为了解决这两个问题，我们首先提出了一种新型的神经网络结构，即样品加权超网络（SWIPENET），以进行小物体检测。 Swipenet由高分辨率和语义丰富的超特征图组成，可以显着提高小物体检测精度。此外，我们提出了一种新型样品加权损失函数，可以对SwipeNet进行模拟样品权重，该样品使用一种新型样品重新加权算法，即逆转多类Adaboost（IMA），以减少噪声对拟议SwipeNet的影响。在两个水下机器人取件比赛数据集的实验urpc2017和urpc2018表明，针对几种最新的对象检测方法，提出的SWIPENET+IMA框架在检测准确性方面取得了更好的性能。
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.
