基于改進YOLOv3目標檢測算法的船舶運載貨物自動識別研究

打開文本圖片集

摘要：船舶貨物自動識別高精度數(shù)據(jù)獲取難，影響檢測性能。該文利用弱監(jiān)督至全監(jiān)督框架，結(jié)合改進算法構(gòu)建組合框架，平均識別精度達32.0%，定位精度達73.8%，高于對比方法。該框架在弱監(jiān)督環(huán)境下表現(xiàn)優(yōu)異，適用于船舶貨物自動識別。

關(guān)鍵詞：YOLOv3；弱監(jiān)督；船舶運載；候選區(qū)域

doi：10.3969/J.ISSN.1672-7274.2024.09.001

中圖分類號：TP 391.41                 文獻標志碼：A            文章編碼：1672-7274（2024）09-000-03

Research on Automatic Identification of Ship Cargo Based on Improved YOLOv3 Object Detection Algorithm

HOU Guojiao1， SUN Rong1， XIAO Shengkui1， LI Wen1， ZHANG Dong2

（1. The Navigation Authority of Yangtze Gorges， Yichang 443002， China;

2. Hunan Tianxiakuan Information Technology Co.， Ltd.， Changsha 410000， China）

Abstract： The difficulty in obtaining high-precision data for automatic identification of ship cargo affects the detection performance. This study utilizes a weak supervision to full supervision framework combined with improved algorithms to construct a combined framework. The average recognition accuracy reaches 32.0%， and the positioning accuracy reaches 73.8%， which is higher than the comparison methods. This framework performs excellently in a weak supervision environment and is suitable for automatic identification of ship cargo.

Keywords： YOLOv3; weak supervision; ship transportation; candidate region

0   引言

在船舶貨物自動識別領(lǐng)域，視覺圖像的檢測識別扮演著核心角色，而人工智能算法的興起為此提供了新的思路[1]。（剩余3662字）