融合混合域注意力的YOLOv4在船舶检测中的应用

计算机与现代化 ›› 2021, Vol. 0 ›› Issue (09): 75-82.

融合混合域注意力的YOLOv4在船舶检测中的应用

(1.中国航天科工集团第二研究院,北京100039;2.北京航天长峰股份有限公司,北京100039)

出版日期:2021-09-14 发布日期:2021-09-14
作者简介:赵玉蓉(1996—),女,陕西榆林人,硕士研究生,研究方向:计算机视觉,深度学习,E-mail: zyr_xx@126.com; 郭会明(1966—),男,研究员,硕士生导师,研究方向:计算机信息系统集成与应用,E-mail: 13901314693@139.com; 焦函(1981—),男,研究员,博士后,研究方向:大数据,人工智能,E-mail: xiaojiaomail@163.com; 章俊伟,男,工程师,硕士,研究方向:边境管控智能应用,E-mail: zjw_jenry@foxmail.com
基金资助:
国家重点研发计划项目(2020YFC0833406)

Application of YOLOv4 with Mixed-domain Attention in Ship Detection

(1. The Second Research Institute of China Aerospace Science and Industry Corporation, Beijing 100039, China;

2. Beijing Aerospace Changfeng Co. Ltd., Beijing 100039, China)

Online:2021-09-14 Published:2021-09-14

摘要/Abstract

摘要： 海上船舶检测在海事监管领域发挥着重要的作用，然而由于海上的复杂环境以及船型的多样性，现有的基于卷积神经网络的方法在船舶检测领域难以同时满足高精度和实时的要求。针对复杂环境下海上船舶实时检测困难的问题，提出一种基于YOLOv4的YOLO-Marine模型，该模型将混合注意力机制引入检测网络的backbone部分，首先使用Mosaic方法对船舶数据进行预处理，然后通过K-Means+〖KG-*3〗+聚类得到初始anchor，并在Darknet上实现模型，用真实船舶数据集对模型进行训练和评估。实验结果表明YOLO-marine与YOLOv4相比，将船舶检测任务的mAP提升了2.1个百分点，在保证实时性的同时有效提高了船舶检测的精度，且在小目标和遮挡目标检测方面效果突出。

关键词: 船舶检测, 数据增强, YOLO, K-means++, 注意力机制

Abstract: Marine ship detection plays an important role in the maritime field. Due to the complicated environment and the diversity of ships, existing methods based on convolutional neural network cannot achieve both high accuracy and real-time performance. To solve the problem of ship’s real-time detection in complicated environment, a YOLO-marine model based on YOLOv4 is proposed in which the domain attention mechanism is introduced into backbone. Firstly, the Mosaic method is used to preprocess the ship data. Then the K-Means++ algorithm is used to get initial anchors. The model is implemented on Darknet for training and evaluation with the real ship dataset. The experimental result show that compared with YOLOv4, YOLO-marine improves the mAP of ship detection task by 2.1 percentage points. The model can effectively improve the accuracy of ship detection while ensuring real-time performance. It also gives outstanding results in small and occluded target.

Key words: ship detection, data enhancement, YOLO, K-means+〖KG-*3〗+, attention mechanism

赵玉蓉, 郭会明, 焦函, 章俊伟. 融合混合域注意力的YOLOv4在船舶检测中的应用[J]. 计算机与现代化, 2021, 0(09): 75-82.

ZHAO Yu-rong, GUO Hui-ming, JIAO Han, ZHANG Jun-wei. Application of YOLOv4 with Mixed-domain Attention in Ship Detection[J]. Computer and Modernization, 2021, 0(09): 75-82.

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