Open AccessSmart Boat Detection Based on Feature Pyramid Network and Deformable Convolution
Author(s) -
Kaipeng Li,
Yunge Wang,
Yuan Shao,
Xingxiao Wu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2083/4/042018
Subject(s) - pyramid (geometry) , convolution (computer science) , feature (linguistics) , computer science , artificial intelligence , submarine , object detection , computer vision , object (grammar) , remote sensing , pattern recognition (psychology) , geology , marine engineering , artificial neural network , engineering , mathematics , linguistics , philosophy , geometry
The submarine cable guarantees the electricity and communication of the island residents. The operation of fishing boats poses a major threat to the submarine cable. Due to the complex environment, manual monitoring has defects such as strong subjective factors and easy fatigue. The paper adopts the intelligent monitoring method, using the object detection algorithm based on deep learning and the camera to monitor the boats on the sea. Use feature pyramid network to enhance the detection of smaller and farther boats. Use deformable convolution to solve the problem of few samples. Experimental results show that model can detect boats. The detection ability of the feature pyramid network is stronger, especially for the distant and smaller boat targets. Using deformable convolution can improve the accuracy of models trained on small dataset.