Multi-Task Learning-Enabled Automatic Vessel Draft Reading for
  Intelligent Maritime Surveillance
Jingxiang Qu Ryan Wen Liu Chenjie Zhao Yu Guo Sendren Sheng-Dong Xu Fenghua Zhu Yisheng Lv
The accurate and efficient vessel draft reading (VDR) is an important
component of intelligent maritime surveillance, which could be exploited to
assist in judging whether the vessel is normally loaded or overloaded. The
computer vision technique with an excellent price-to-performance ratio has
become a popular medium to estimate vessel draft depth. However, the
traditional estimation methods easily suffer from several limitations, such as
sensitivity to low-quality images, high computational cost, etc. In this work,
we propose a multi-task learning-enabled computational method (termed MTL-VDR)
for generating highly reliable VDR. In particular, our MTL-VDR mainly consists
of four components, i.e., draft mark detection, draft scale recognition,
vessel/water segmentation, and final draft depth estimation. We first construct
a benchmark dataset related to draft mark detection and employ a powerful and
efficient convolutional neural network to accurately perform the detection
task. The multi-task learning method is then proposed for simultaneous draft
scale recognition and vessel/water segmentation. To obtain more robust VDR
under complex conditions (e.g., damaged and stained scales, etc.), the accurate
draft scales are generated by an automatic correction method, which is
presented based on the spatial distribution rules of draft scales. Finally, an
adaptive computational method is exploited to yield an accurate and robust
draft depth. Extensive experiments have been implemented on the realistic
dataset to compare our MTL-VDR with state-of-the-art methods. The results have
demonstrated its superior performance in terms of accuracy, robustness, and
efficiency. The computational speed exceeds 40 FPS, which satisfies the
requirements of real-time maritime surveillance to guarantee vessel traffic
safety.