Research on Automatic Identification of Ship Cargo Based on Improved YOLOv3 Object Detection Algorithm
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.
YOLOv3weak supervisionship transportationcandidate region
NETL
NSTL
万方数据
