论文标题
基于联合自我监督的脱毛和改进的空间变压器网络的高精度水下对象检测
A high-precision underwater object detection based on joint self-supervised deblurring and improved spatial transformer network
论文作者
论文摘要
基于深度学习的水下对象检测（UOD）仍然是一个重大挑战，因为可见性降低和难以获得从各个角度捕获的足够的水下对象图像进行培训。为了解决这些问题，本文介绍了基于联合自我监督的Deblurring和改进的空间变压器网络的高精度UOD。自我监督的Deblurring子网被引入设计的多任务学习辅助对象检测体系结构，以迫使共享的特征提取模块以输出清洁特征以进行检测子网。为了减轻从不同角度来减轻照片不足的局限性，改进的空间变压器网络是基于透视转换而设计的，从而自适应地丰富了网络中的图像特征。实验结果表明，在URPC2017和URPC2018中的70.3 MAP中实现了47.9所提出的UOD方法，表现优于许多最新的UOD方法，并表明该方法更适合UOD。
Deep learning-based underwater object detection (UOD) remains a major challenge due to the degraded visibility and difficulty to obtain sufficient underwater object images captured from various perspectives for training. To address these issues, this paper presents a high-precision UOD based on joint self-supervised deblurring and improved spatial transformer network. A self-supervised deblurring subnetwork is introduced into the designed multi-task learning aided object detection architecture to force the shared feature extraction module to output clean features for detection subnetwork. Aiming at alleviating the limitation of insufficient photos from different perspectives, an improved spatial transformer network is designed based on perspective transformation, adaptively enriching image features within the network. The experimental results show that the proposed UOD approach achieved 47.9 mAP in URPC2017 and 70.3 mAP in URPC2018, outperforming many state-of-the-art UOD methods and indicating the designed method is more suitable for UOD.
